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Record ID: 70 [ Page 4 of 6, No. 1 ]
Nonparametric Bootstrap Test in a Multivariate Spatial-Temporal Model: A Simulation Study
Authors: Abubakar S. Asaad; Erniel B. Barrios
Abstract:
The assumptions of constant characteristics across spatial locations and constant characteristics across time points facilitates estimation in a multivariate spatial-temporal model. A test based on the nonparametric bootstrap in proposed to verify these assumptions. The simulation studies confirm that the proposed test procedures are powerful and correctly sized.
Keywords: coverage probability, robustness, spatial-temporal model
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Year: 2015 Vol.: 64 No.: 2
Record ID: 69 [ Page 4 of 6, No. 2 ]
Statistics for Applied Researchers: Bootstrap to the Rescue
Authors: Nabendu Pal; Suntaree Unhapipat
Abstract:
Availability of latest fast and affordable computing resources has empowered the statisticians tremendously. This has also given the applied researchers a unique edge to extend the frontier of their knowledge-base by taking advantage of sophisticated computational statistical tools where theoretical derivations of complex sampling distributions are often not required or can be bypassed. ‘Bootstrap method’ is one such tool which is being used widely in solving real-life problems that involve statistical inferences. This article is designed to present bootstrap in simple terms for the applied researchers with useful examples and show how it can go a long way in settling contentious issues with reasonably convincing results.
Keywords: Sampling distribution, p-value, nonparametric bootstrap, parametric bootstrap, test statistic.
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Year: 2015 Vol.: 64 No.: 1
Record ID: 68 [ Page 4 of 6, No. 3 ]
Developed Sampling Strategy in Evaluating Teaching Performance Through Student Ratings
Authors: James Roldan S. Reyes; Zita VJ. Albacea
Abstract:
This paper presents an alternative method apart from the current online or electronic approach, which is currently being used by some higher education institutions (HEIs), in administering student ratings for teachers. The developed method still employed the traditional paper approach but has been improved through the use of sampling application which includes sampling design, sample size, estimation technique, and strategic implementation. Three basic sampling designs such as simple random, stratified random, and cluster sampling were applied at three different sampling rates such as 25%, 50%, and 75%. For the empirical evaluation of the developed method, the Student Evaluation of Teachers (SET) of the University of the Philippines Los Baños (UPLB) was utilized using bootstrap resampling technique. Based on findings, stratified random sampling is the most appropriate sampling design to use with 50% of the students for each class section serving as SET evaluators. Results also revealed that bootstrap estimates of standard error are lower than that of the standard error using jackknife resampling procedure. Generally, the improved traditional paper approach same with the electronic approach could reduce the cost of administering student ratings. However, the electronic approach has a dilemma with regards to high non-response bias leading to invalid results. Thus, to minimize non-response error of the developed method, its standard protocol to administer the student ratings has been formulated.
Keywords: student ratings, traditional paper approach, sampling application, bootstrap resampling, jackknife resampling, non-response error
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Year: 2015 Vol.: 64 No.: 1
Record ID: 67 [ Page 4 of 6, No. 4 ]
Forecasting Time-Varying Correlation Using the DCC Model
Authors: John D. Eustaquio; Dennis S. Mapa; Miguel C. Mindanao; Nino I. Paz
Abstract:
Hedging strategies have become more and more complicated as assets being traded have become more interrelated to each other. Thus, the estimation of risks for optimal hedging does not involve only the quantification of individual volatilities but also include their pairwise correlations. Therefore a model to capture the dynamic relationships is necessary to estimate and forecast correlations of returns through time. Engle'ss dynamic conditional correlation (DCC) model is compared with other models of correlation. Performance of the correlation models are evaluated in this paper using only the daily log returns of the closing prices from January, 2000 to February, 2010 of the Peso-Dollar Exchange Rate and Philippine Stock Exchange index. Ultimately, Engle's DCC model is adopted because of its consistency with expectations. Though generally negative, correlation between these two returns is not really constant as the results indicated. The forecast evaluation of the models was divided into in-sample and out-of-sample forecast performance with short-term (i.e., 22-day, 60-day, and 125-day) and medium-term (250-day and 500-day) rolling window correlations, or realized correlations, as proxies for the actual correlation. Based on the root mean squared error and mean absolute error, the integrated DCC model showed optimal forecast performance for the in-sample correlation patterns while the mean-reverting DCC model had the most desirable forecast properties for dynamic long-run forecasts. Also, the Diebold-Mariano tests showed that the integrated DCC has greater predictive accuracy in terms of the 3-month realized correlations than the rest of the models.
Keywords: dynamic conditional correlation, Peso-Dollar exchange rate, PSE index, hedging
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Year: 2015 Vol.: 64 No.: 1
Record ID: 66 [ Page 4 of 6, No. 5 ]
Classification and Prediction of Suicidal Tendencies of the Youth in the Philippines: An Empirical Study
Authors: Stephen Jun V. Villejo
Abstract:
This paper investigates suicidal tendencies of youth in the Philippines based on the Young Adult Fertility and Sexuality Study (YAFS) 2002. The main goal of the paper is the classification and prediction of suicidal tendencies using classification algorithms. The different classification algorithms such as Classification and Regression Trees, random forests and conditional inference trees; and the logistic regression have consistent findings on the significant variables affecting suicidal tendencies. Due to the severely unbalanced classes of the response variable, the classification models have very poor predictive ability for the minority class although the over-all classification rate is high. A classification algorithm is proposed which improves the predictive ability in terms of balancing out the correct classification in the two classes of the response variable.
Keywords: classification, suicide, prediction, logistic regression
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Year: 2015 Vol.: 64 No.: 1
Record ID: 65 [ Page 4 of 6, No. 6 ]
Comparison of Tree-Based Methods in Identifying Factors Influencing Credit Card Ownership and Prediction Accuracy
Authors: Karl Anton M. Retumban
Abstract:
Factors influencing credit card ownership were identified using the data from Global Financial Inclusion Index Database of The World Bank and the tree-based methods: CART, boosting, and bagging. The prediction accuracy of the methods was compared in terms of the training and test error rate. Results on the world and Philippine data were compared. The factors influencing consumers to own a credit card are financial account ownership, highest educational attainment and age. This is the case both for the World and Philippine data. For the World data, the factors that influence credit card ownership are financial account ownership, debit card ownership, withdrawal frequency in personal account, highest educational attainment, current loan for home or apartment purchase, age, get cash in ATM and deposit cash in ATM. For the Philippine data, the influential factors to Filipino consumers are financial account ownership, age, income quintile, highest educational attainment, and deposit cash over the counter in branch of bank or financial institution. Among the procedures, boosting has the smallest test error rate while bagging has the largest training and test error rate, both for the world and Philippine data. CART and boosting has the smallest training error rate under the world data and Philippine data respectively.
Keywords: classification and regression trees, boosting, bagging, credit card ownership, Global Financial Inclusion Index Database
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Year: 2015 Vol.: 64 No.: 1
Record ID: 64 [ Page 4 of 6, No. 7 ]
Predicting Socioeconomic Classification in the Philippines: Beyond the Ordinal Logistic Regression Model
Authors: Michael Daniel C. Lucagbo
Abstract:
Socioeconomic classification (SEC) is an important construct to enable one to capture and understand changes in the structure of a society. The 1SEC 2012, a new scheme for identifying the SEC of Philippine households, predicts SEC using information on household characteristics through the ordinal logistic regression model. This study aims to improve the predictive ability of the 1SEC methodology by using state-of-the-art statistical learning techniques: discriminant analysis, support vector machines (SVM), and artificial neural networks (ANN), and thereby suggest a new scheme for predicting SEC. The results show that SVM and ANN exhibit improvements in exact-cluster prediction performance, suggesting alternative methods for predicting SEC.
Keywords: socioeconomic classification, ordinal logistic regression, discriminant analysis, support vector machines, artificial neural networks
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Year: 2015 Vol.: 64 No.: 1
Record ID: 63 [ Page 4 of 6, No. 8 ]
Determinants of income class in Philippine households: Evidence from the Family Income and Expenditure Survey 2009
Authors: Stephen Jun Villejo; Mark Tristan Enriquez; Michael Joseph Melendres; Dexter Eric Tan; Peter Julian Cayton
Abstract:
The government has instituted projects aimed at helping the poor, and has implemented mechanisms to make the services accessible to them. The wisdom of the projects of the government should not be defeated by misidentification of deserving households to enjoy those projects which could be remedied through proper and thorough assessment of their economic status.The study aims to provide a methodology and model for classifying households using demographic and household assets that may be used in identifying recipients of poverty-targeted projects. Cluster analysis was employed to identify household classification using income data from the Family Income and Expenditure Survey 2009. Five income clusters were identified. To study the relationship between the income classes and several predictors of income identified from previous researches, a family of logistic regression models have been utilized, culminating to the generalized logistic regression model. Nine significant predictors were included in the final reduced model. The model is assessed to have good fit via multiple Hosmer and Lemeshow tests. These variables were the following: location of the household whether in NCR or not, or in urban or rural area; education and employment status of the household head; number of cars, air-conditioners, and television sets; and the building type and household type. The sensitivity table suggests that the model is biased towards predicting the lower income classes. The research has identified a viable methodology for classification of income classes for households.
Keywords: mutlinomial logistic model, income determinants, clustering methods
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Year: 2014 Vol.: 63 No.: 2
Record ID: 62 [ Page 4 of 6, No. 9 ]
Determinants of regional minimum wages in the Philippines
Authors: Lisa Grace S. Bersales; Michael Daniel C. Lucagbo
Abstract:
In the Philippines, the National Wages and Productivity Commission (NWPC) formulates policies and guidelines that Tripartite Wage and Productivity Boards use in determining minimum wages in their respective regions. Reviews of the implementation of the minimum wage determination have been done in past studies to determine which of the factors listed by NWPC for consideration by the wage boards are actually used to determine minimum wage. Results indicated that the significant determinant of minimum wage is consumer price index. Two stage least squares estimation of a Fixed Effects Model for Panel Data for the period 1990-2012 showed that significant determinants of regional minimum wage for non-agriculture are: Consumer Price Index, Gross Regional Domestic Product, and April employment rate. The lower and upper estimates from the estimated equation of the Fixed Effects Model for Panel Data may provide intervals that the wage boards can use in making the final determination of minimum wage. The following shocks which would likely introduce abnormal wage setting behavior on the part of the wage boards were not significant: 1997-1998 - Asian Financial Crisis; 2002 - spillover effects from U.S. technology bubble burst; 2008-2009 - spillover effects from Global Financial Crisis.
Keywords: tripartite wage and productivity boards, minimum wage, fixed effects models for panel data, shocks, two stage least squares, fixed effects model for panel data
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Year: 2014 Vol.: 63 No.: 2
Record ID: 61 [ Page 4 of 6, No. 10 ]
The link between expenditure on contraceptives and number of young dependents in the Philippines
Authors: Michael Daniel C. Lucagbo; Genica Peye C. Alcaraz; Kristina Norma B. Cobrador; Elaine Japitana; Gelli Anne Q. Sadsad
Abstract:
The growing population of the Philippines hinders the country from achieving economic development due to the limited resources available. The 2010 Census on Population and Housing (CPH) reports that the Philippine population has struck 92.1 million, a 15.8-million increase from the 76.3 million population size reported in 2000. Moreover, the relationship between population and family size, on the one hand, and poverty incidence on the other, has been established through econometric models showing the causality between presence of young dependents in a household and household welfare. Using the Family Income and Expenditure Survey (FIES) 2009 data, this study examines the factors affecting the number of young dependents in a household, and focuses in particular on the household’s level of contraceptive expenditure. The negative binomial regression model is used to quantify the effect of the factors and predict the average number of young dependents in a household. This model allows for overdispersion in the data. Results show that for every P10,000 increase in total expenditure on contraceptives for a period of six months, the mean number of young dependents decreases by 3.7%. Other demographic variables such as education of household head and income of the household are controlled for in the study.
Keywords: Young dependents, contraceptive expenditure, negative binomial regression, overdispersion
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Year: 2014 Vol.: 63 No.: 2
Record ID: 60 [ Page 4 of 6, No. 11 ]
Biosurveillance of measles using control charts: A case study using NCR laboratory confirmed measles counts from January 2009 to January 2014
Authors: Lorraine Christelle B. Angkico; Priscilla A. Diaz; Robert Neil F. Leong; Frumencio F. Co
Abstract:
This paper aims to explore early outbreak detection methods for measles. Two methods adopted from statistical process control were modified and used to fit biosurveillance, namely Shewhart and Exponentially Weighted Moving Average (EWMA) charts. Seven variations of such control charts are proposed: two under Shewhart chart (normal-based and zero-inflated Poisson (ZIP)-based) and five under EWMA charts (?s of 0.05, 0.10, 0.15, 0.20, and 0.25). To study the proposed charts, daily counts of laboratory confirmed cases of measles in the National Capital Region from 2009 until 2014 were utilized to characterize both the disease background and outbreak equations. During this time span, three measles outbreaks have transpired. The proposed charts, set at average time between false signals (ATFSs) of both one and two months, were evaluated and compared using performance metrics such as conditional expected delay (CED), proportion of true signals (PTS), proportions of detections in an outbreak (PDO), and probability of successful detection (PSD), computed from 500 sets of simulated data. It was found that ZIP-based Shewhart and EWMA with a ? of 0.05 work best for ATFSs of one and two months, respectively. Health-governing bodies may seek to explore the possible utilization of these charts to improve measles surveillance.
Keywords: control charts, measles, early event detection, biosurveillance
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Year: 2014 Vol.: 63 No.: 2
Record ID: 59 [ Page 4 of 6, No. 12 ]
An efficient variant of dual to ratio and product estimator in sample surveys
Authors: Gajendra K. Vishwakarma; Raj K. Gangele; Ravendra Singh
Abstract:
In this paper, we propose a dual to ratio and product estimator for estimating finite population mean of study variable on applying simple transformation to auxiliary variable by using its average values in the population that are generally available in practice. The mean squared error of the proposed estimator have been obtained to the first degree of approximation. It has also been shown that the proposed estimator has greater applicability and is more efficient than the usual estimator even when, the existing estimators are less efficient. An empirical study is carried out to demonstrate the performance of proposed estimator.
Keywords: Auxiliary variable, Study variable, Mean square error, Population mean, Simple random sampling
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Year: 2014 Vol.: 63 No.: 2
Record ID: 58 [ Page 4 of 6, No. 13 ]
A general class of chain ratio-product type exponential estimators in double sampling using two auxiliary variates
Authors: Gajendra K. Vishwakarma; Manish Kumar; Raj K. Gangele
Abstract:
In this paper, a general class of chain ratio-product type exponential estimators has been proposed for estimating a finite population mean in presence of two auxiliary variates under double sampling scheme. The expressions for bias and mean square error (MSE) of the proposed class are derived up to the first degree of approximation. Also, the expression of asymptotic optimum estimator (AOE) in the proposed class is obtained. Some estimators are shown to be particular members of the proposed class. The proposed class has been compared for its precision with the usual unbiased estimator and several other estimators of the literature. In addition, an empirical study is also carried out in support of theoretical findings.
Keywords: Auxiliary variates, Study variate, Double Sampling, bias, mean square error.
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Year: 2014 Vol.: 63 No.: 2
Record ID: 57 [ Page 4 of 6, No. 14 ]
Modeling clustered survival data with cured fraction
Authors: Iris Ivy M. Gauran; Angela D. Nalica
Abstract:
In modelling lifetime data, standard parametric theory assumes that all observations will eventually experience the event of interest if they are monitored for a very long period. While every unit starts as susceptible to the event of interest, a fraction of observations may switch into a non-susceptible group. A mixture cured fraction model with covariates is modified to incorporate random clustering effect to characterize the switch mechanism. Simulation studies and telecommunications data show that cured fraction models with random clustering effect perform better than their parametric counterpart in terms of predictive ability. Moreover, results show that the nonparametric method is superior than modified parametric Cox PH model.
Keywords: Mixture Cured Fraction Models, Random Clustering Effect, Right-censored Lifetime Data
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Year: 2014 Vol.: 63 No.: 2
Record ID: 56 [ Page 4 of 6, No. 15 ]
Proceedings of the Focused Group Discussion on Accreditation/Certification for Professional Statisticians
Authors: PSAI Initiatives
Abstract:
FOREWORD
The Philippine Statistical Association, Inc. (PSAI) is a professional association dedicated to the promotion of Statistics as a science and a discipline. As such, it recognizes the need to pursue the development of the discipline and the continuing professional growth of its practitioners in the academe, the government and private sectors, and in the international community.
In 2008, the PSAI through the Institutional Development Committee (IDC) chaired by Mr. Tomas P. Africa, then Vice President and Chair of the IDC pursued the crafting and ratification of the Code of Ethics for Statisticians, and notes in the Foreword that:
"It has been an aspiration of the Philippine Statistical Association (PSA) to institute a system of accreditation or certification for Statistics professionals, similar to those existing in Australia, New Zealand, the United Kingdom and the United States. On at least two fronts, the label 'statistician' may have been misused and misappropriated by unscrupulous professionals.
The accreditation stage will deal with what would be the qualifications: education, work experience, research record as well as the behavior or ethical standards of the statistics practitioner. This Code addresses the latter. The necessary academic background, and work experience needed to bring about the conduct and/or behavior of such professionals may be deduced from this Code."
With the Code of Ethics for Statisticians firmly in place, the stage is set for the accreditation process. Under the same stewardship, Mr. Africa as Vice President and Chair of the Institutional Development Committee (2012-2013), concerned professionals were gathered to undertake the Focus Group Discussion (FGD), and to put into motion the work envisioned to initiate the development of a system for eventual accreditation and professional certification of practitioners in the statistics profession.
Keywords:
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Year: 2014 Vol.: 63 No.: 1
Record ID: 55 [ Page 4 of 6, No. 16 ]
Indentifying Influencers of Consumer Activity: A Case Study in Predictive Modeling
Authors: Angela D. Nalica; Joseph Ryan G. Lansangan
Abstract:
Marketing activation usually entails a universal blast of information to all consumers. Oftentimes, only a small proportion of the consumers react positively to such activation, resulting to waste in marketing expenses. If a circle of influencers can be identified for certain events or phenomena, then such activities can be focused into a group of factors or individuals, thus, optimizing the outcomes. With the identification of such group of influencers, resources for strategic optimization of outcomes can be allocated efficiently. A usage database is used to identify consumers who could initiate or influence the complex dynamics of consumer behavior. The data mining process of clustering, sampling, aggregation, modeling, and validation are used to mine such information from the database.
Keywords: logistic regression, segmentation, influencers, consumer behaviour, customer relationship management
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Year: 2014 Vol.: 63 No.: 1
Record ID: 54 [ Page 4 of 6, No. 17 ]
Effects of Household Use of Biomass Fuel and Kerosene on Birth Weight of Babies in the Philippines
Authors: Michael Daniel C. Lucagbo
Abstract:
Birth weight is an important indicator of a child’s health status. It is a significant factor of his or her risk of mortality and morbidity. Infants with low birth weight have been shown to be 40 times more likely to die within the first 28 days of birth than normal birth weight infants. Moreover, low birth weight infants exhibit a much higher incidence of neurological impairment, gross and fine motor dysfunction and developmental delay. Instead of going down to reduce the incidence of child mortality (which is one of the Millennium Development Goals), the incidence of low birth weight in the Philippines has gone the opposite direction: rising from 20.3% in 2003 to 21.2% in 2008. This paper tackles the very serious issue of birth weight using data from the 2008 National Demographic and Health Survey (NDHS), and focuses on one important risk factor: type of cooking fuel used in the household. Using the ordinal logistic regression model, the study establishes that the use of dirty cooking fuel (biomass fuel or kerosene) for daily use of cooking and heating is a significant environmental risk factor of low birth weight. Moreover, the results also show that maternal smoking is significantly associated with the size of the child at birth. Other demographic factors that may be associated with low birth weight are examined as well. Information about the effect cooking fuel on birth weight should lead the government and policymakers to make clean cooking fuel available to Philippine households at a cheap cost.
Keywords: Low birth weight, biomass fuel, maternal smoking, ordinal logistic regression
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Year: 2014 Vol.: 63 No.: 1
Record ID: 53 [ Page 4 of 6, No. 18 ]
Comparison of Different Methods of Constructing Housing Start Index in the Philippines
Authors: Felicidad Hebron
Abstract:
We investigate three methods of constructing housing start index with a fixed base year. In the Philippines, researchers and planners uses data on building permits to monitor construction sites where economic activities are expected to follow. Suppliers of construction materials such as cement, lumber, steel, among others, rely on these data for planning purposes. Other businesses like banks and food chains also use these data as proximate indicators of supply and demand for investment. A mixed model accounting the empirical relations between the index and other economic indicators they usually lead is used in the assessment of the index resulting from three different methods. There is a strong space-time association between the index and other indicators, confirming the relationship between the economic boom and housing start index. There is evidence that the index is capable of leading some key economic indicators.
Keywords: housing start index, leading indicators, mixed models
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Year: 2014 Vol.: 63 No.: 1
Record ID: 52 [ Page 4 of 6, No. 19 ]
Design Strategies in Fitting a Nonlinear Model
Authors: Michael Van Supranes
Abstract:
Estimation of parameters in a nonlinear model depends on the distribution of data points along various levels of curvature in the function to be estimated. Using Monte Carlo simulation, an optimal allocation procedure for building stratified designs was derived. The optimal allocation procedure conforms well to a proportionality property, directly relating the number of observations with the total curvature and measure or length of the domain. The proportionality property can be used to easily construct an allocation procedure that is near the optimal. Stratification results were applied and explored on uniform designs. Simulation results show that strategic stratification can improve the prediction accuracy of uniform designs.
Keywords: Stratification, Experimental Designs, Spline Regression, Monte Carlo Simulation
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Year: 2014 Vol.: 63 No.: 1
Record ID: 51 [ Page 4 of 6, No. 20 ]
Semiparametric Poisson Regression Model for Clustered Data
Authors: Eiffel A. de Vera
Abstract:
A semiparametric Poisson regression is proposed in modeling spatially clustered count data. The heterogeneous covariate effect across the clusters is formulated in the context of nonparametric regression while the random clustering effect is based on a parametric specification. We propose two estimation procedures: (1) the parametric and nonparametric parts are estimated simultaneously via penalized least squares; and (2) the parametric and nonparametric parts are estimated iteratively via the backfitting algorithm. The simulation study exhibited the advantages of these two methods over ordinary Poisson regression and an intrinsically linear model when the aggregate covariate effect is negligible. This happens when sensitivity to the covariate is minimal or the data-generating model is not linear. The two estimation methods are generally more advantageous over the traditional approaches when linear model fit is poor. In cases where the linear fit is good, the proposed methods are at par with the traditional methods, but the second approach can still be advantageous when there are several covariates involved since the backfitting algorithm yields computational simplicity in the estimation process.
Keywords: backfitting, generalized additive models, nonparametric regression, random effects
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Year: 2014 Vol.: 63 No.: 1
Record ID: 50 [ Page 4 of 6, No. 21 ]
Modelling Zero-Inflated Clustered Count Data: A Semiparametric Approach
Authors: Kevin Carl P. Santos
Abstract:
This paper proposes to use an additive semiparametric Poisson regression in modelling zero-inflated clustered data. Two estimation methods are exploited in this paper based on de Vera (2010). The first simultaneously estimates both the parametric and nonparametric parts of the model. The second utilizes the backfitting algorithm by smoothing the nonparametric function of the covariates and then estimating the parametric parts of the postulated model. The predictive accuracy, measured in terms of root mean square error (RMSE), of the proposed methods is compared to that of ordinary Zero-Inflated Poisson (ZIP) regression model. It is found out through simulation study that the average RMSE of the ordinary ZIP regression model is at most 81% and 27% higher for equal and unequal cluster sizes, respectively, than that of proposed model whose parametric and nonparametric parts are simultaneously estimated.
Keywords: Zero-Inflated Poisson models, clustered data, Generalized Additive Models, backfitting algorithm
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Year: 2014 Vol.: 63 No.: 1
Record ID: 49 [ Page 4 of 6, No. 22 ]
Autologistic Spatial-Temporal Modeling
Authors: Ma. Andriena Ida B. Del Ayre-Ofina
Abstract:
We postulate a combination of spatial-temporal and autologistic model in characterizing binary data collected over time and space. Using a second-order neighborhood system in defining the spatial component of the model, backfitting algorithm is used in estimating the model. As the incidence of success and failure responses becomes balanced, sensitivity and specificity increases. The predictive ability of the model is fairly robust to the spatial parameter but is significantly influenced by the temporal parameter. The bias of the estimate for the spatial parameter declines as it becomes dominant into the model. Furthermore, as the autocorrelation becomes stronger, its estimate becomes less biased. The backfitting algorithm is also observed to converge fast in the estimation of the spatial-temporal autologistic model.
Keywords: binary response, autologistic model, spatial-temporal model, backfitting
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Year: 2014 Vol.: 63 No.: 1
Record ID: 48 [ Page 4 of 6, No. 23 ]
Visual Exploration of Climate Variability
Authors: Wendell Q. Campano; Rona Mae U. Tadlas
Abstract:
In this paper, a data visualization framework for investigating and exploring climate time series data is introduced. This method utilizes the results obtained from performing series of cluster analysis based on a particular multivariate data set for each defined subset in the time series. The said approach is implemented to the climate data in the Philippines. The data image results obtained from the procedure revealed the expected overall climate pattern in the Philippines as well as some localized segments of climate changes in the time series which deviate from the overall pattern. A wavelet analysis which is a well established method in analyzing climate data is also done to validate the results shown by the proposed visualization method.
Keywords: information visualization; data image; cluster analysis; wavelet; climate change; climate variability; time series; multivariate data
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Year: 2013 Vol.: 62 No.: 2
Record ID: 47 [ Page 4 of 6, No. 24 ]
Measuring Income Mobility using Pseudo-Panel Data
Authors: Arturo M. Martinez Jr; Mark Western; Michele Haynes; Wojtek Tomaszewski
Abstract:
To reconcile the need of providing a more dynamic perspective of the evolution of income distribution with the lack of panel data, several techniques have been offered to construct pseudo-panel data from repeated cross-sectional surveys. Using actual panel data from the Philippines, this study evaluates the performance of four pseudo-panel techniques in measuring a wide array of income mobility indicators. Preliminary results suggest that methods with more flexible income model specifications perform better than those with highly parameterized models. More importantly, these flexible pseudo-panel procedures produced estimates of poverty dynamics and movement-based indices which are quite close to the estimates computed from the actual panel data. Nevertheless, further improvements are warranted to be able to develop a more satisfactory estimation procedure for indices measuring temporal dependence and the inequality-reducing effect of income mobility.
Keywords: panel survey; cross-sectional survey; temporal; dependence; income distribution
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Year: 2013 Vol.: 62 No.: 2
Record ID: 46 [ Page 4 of 6, No. 25 ]
Effects of Education on Climate Risk Vulnerability in the Philippines: Evidence from Regional Panel Data
Authors: Michael Daniel C. Lucagbo; Kristina Norma B. Cobrador; Nikki Ann M. de Mesa; Remy Faye M. Ferrera; Jennifer E. Marasigan
Abstract:
The effects of climate change are being felt disproportionately in the world’s poorest countries and among those groups of people least able to cope. The Philippines, being a storm-lashed nation, is one country having high climate change vulnerability and low climate change resilience. A number of researches have suggested investments on adaptation which place strong emphasis on reducing vulnerability to climate change. Focusing on climate change vulnerability in the Philippines, this study examines the effect of one particular type of government intervention: increasing the level of education. In this study, the effect of education on vulnerability to climate change is examined in a regional panel data analysis using official Philippine statistics from the Natural Disaster Risk Reduction and Management Council (NDRRMC), Labor Force Survey (LFS), National Statistical Coordination Board (NSCB). Using the fixed-effects Poisson (FEP) regression model, the study establishes that at the community level, the number of employed college graduates is a significant factor that reduces climate risk vulnerability (measured by a number of deaths from natural disasters), controlling for other factors such as number of disasters, gross regional domestic product (GRDP), and population size.
Keywords: Vulnerability, Resilience, Panel Data, Fixed-effects Poisson model
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Year: 2013 Vol.: 62 No.: 2
Record ID: 45 [ Page 4 of 6, No. 26 ]
Regression Analyses of the Philippine Birth Weight Distribution
Authors: Elline Jade Beltran; Robert Neil F. Leong; Frumencio F. Co
Abstract:
Low birth weight has both short-term and long-term effects. It can lead to complications among infants causing neonatal deaths. Several literatures also suggested relationships between low birth weight and delayed mental and physical development. These negative effects are further magnified in developing countries, one of which is the Philippines. In this paper, birth weight is analysed through logistic, ordinary least squares, and quantile regression techniques using a sample from the 2008 Philippine Birth Recode. Quantile regression results offer a more dynamic picture of how these correlates affect the conditional distribution of birth weight. The obtained estimates of the marginal effects of several demographical and maternal health correlates of birth weight suggest that socially and economically impoverished mothers are more likely to have low birth weight babies. These results would recommend a focus on improving maternal health care through proper education.
Keywords: birth weight; quantile regression; logistic regression; ordinary least squares
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Year: 2013 Vol.: 62 No.: 2
Record ID: 44 [ Page 4 of 6, No. 27 ]
Profitability and Growth Topology Analysis of Unilevel-type of Network Marketing Structures
Authors: John Carlo P. Daquis; Angelique O. Castaneda; Nelson D. Sy; Joseph V. Abgona
Abstract:
This study analyzes a type of multi-level marketing (MLM) structure through a simulation of MLM systems. In unilevel MLM, distributors earn from both sales from direct selling and commissions from recruitment of downlines. Several distributional assumptions were made in constructing the system, such as the use of the uniform, Bernoulli, and Poisson distributions. Member income is measured based on commission from recruit pay-ins in their downlines and income from direct selling. Based on the simulated unilevel MLM structures, the fundamental behavior of a unilevel MLM is captured and analyzed in terms of its network growth topology and profitability.
Keywords: multi-level marketing; network simulation; unilevel structure; complex systems; probability distributions
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Year: 2013 Vol.: 62 No.: 2
Record ID: 43 [ Page 4 of 6, No. 28 ]
Classification of Congenital Hypothyroidism using Artificial Neural Networks
Authors: Iris Ivy Gauran; Ma. Sofia Criselda A. Poblador
Abstract:
The Newborn Screening Reference Center (NSRC) of the National Health Institute in University of the Philippines Manila collects measurements from five attributes to determine whether Congenital Hypothyroidism (CH) is present in a neonate. Detecting the CH cases is a major concern of medical practitioners because it provides richer information than the healthy ones. However, because of the rarity of this metabolic condition, existing classification algorithms oftentimes misclassify a newborn as “normal” even if it is not. This paper investigates the efficiency of Self-Organizing Kohonen Maps (SOM), a type of artificial neural network. Though it is a visualization and clustering tool, the researchers want to probe on its ability to detect outliers and properly classify a newborn as normal or not by coming up with a statistically computed threshold value. Instead of working directly with the original attributes of the data, a reduced set of SOM prototypes is utilized to represent the data in a space of smaller dimension, seeking to preserve the probability distribution and topology of the input space. Results showed a misclassification rate of 13.5%. Though it is found to be slightly less superior to the existing classification rules, the proposed methodology was able to address the problem of finding a statistical threshold value. Also, the methodology verifies that age has a major effect on misclassifying “Normal” as “Abnormal” since postponement of newborn screening to a later age causes the quantization error to boost drastically, hence, easily exceeding the value of the first decision threshold.
Keywords: self-organizing kohonen maps (SOM), classification algorithm, outlier detection, newborn screening for congenital hypothyroidism
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Year: 2013 Vol.: 62 No.: 2
Record ID: 42 [ Page 4 of 6, No. 29 ]
Career opportunities in the pharmaceutical industry
Authors: Jennifer Ly
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Year: 2013 Vol.: 62 No.: 1
Record ID: 41 [ Page 4 of 6, No. 30 ]
An elementary proof of independence of least squares estimation of regression coefficients and of variance in linear regression
Authors: Alexaander R. De Leon; Joyce Raymund B. Punzalan
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Year: 2013 Vol.: 62 No.: 1