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Record ID: 40 [ Page 7 of 8, No. 1 ]
High dimensional nonparametric discrete choice model
Authors: Maureen Dinna D. Giron
Abstract:
The functional form of a model can be a constraint in the correct prediction of discrete choices. The fl exibility of a nonparametric model can increase the likelihood of correct prediction. The likelihood of correct prediction of choices can further be increased if more predictors are included, but as the number of predictors approaches or exceeds the sample size, more serious complications can be generated than the improvement in prediction. With high dimensional predictors in discrete choice modeling, we propose a generalized additive model (GAM) where the predictors undergo dimension reduction prior to modeling. A nonparametric link function is proposed to mitigate the deterioration of model fi t as a consequence of dimension reduction. Using simulated data with the dependent variable having two or three categories, the method is comparable to the ordinary discrete choice model when the sample size is suffi ciently large relative to the number of predictors. However, when the number of predictors exceeds substantially the sample size, the method is capable of correctly predicting the choices even if the components included in the model account for only 20% of the total variation in all predictors.
Keywords: discrete choice model; generalized additive model; high dimensional data; nonparametric model
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Year: 2013 Vol.: 62 No.: 1
Record ID: 39 [ Page 7 of 8, No. 2 ]
Esstimation under purposive sampling with auxiliary variable
Authors: John Erwin Banez
Abstract:
A PPS purposive selection and estimation was studied. A purposive sampling proposed by Guarte and Barrios (2006) is used. Instead of SRS, PPS with auxiliary variable was used in the selection. Results were compared to SRS estimates and SRS purposive. Standard error and coefficient of variation of estimates were basis for comparison. It was shown that PPS purposive has comparable results with SRS purposive and both have better performance compared to SRS.
Keywords: purposive sampling; bootstrap; auxiliary variable
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Year: 2013 Vol.: 62 No.: 1
Record ID: 38 [ Page 7 of 8, No. 3 ]
Sparse principal component regression
Authors: Joseph Ryan G. Lansangan
Abstract:
Modeling of complex systems is usually confronted with high dimensional independent variables. Econometric models are usually built using time series data that often exhibit nonstationarity due to the impact of some policies and other economic forces. Both cases are usually affected by the multicollinearity problem resulting to unstable least squares estimates of the linear regression coefficients. Principal component regression can provide solution, but in cases where the regressors are nonstationary or the dimension exceeds the sample size, principal components may yield simple averaging of the regressors and the resulting model is difficult to interpret due to biased estimates of the regression coefficients. A sparsity constraint is added to the least squares criterion to induce the sparsity needed for the components to reflect the relative importance of each regressor in a sparse principal component regression (SPCR) model. Simulated and real data are used to illustrate and assess performance of the method. SPCR in many cases leads to better estimation and prediction than conventional principal component regression (PCR). SPCR is able to recognize relative importance of indicators from the sparse components as predictors. SPCR can be used in modeling high dimensional data, as an intervention strategy in regression with nonstationary time series data, and when there is a general problem of multicollinearity.
Keywords: sparsity; high dimensionality; multicollinearity; nonstationarity; sparse principal components
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Year: 2013 Vol.: 62 No.: 1
Record ID: 37 [ Page 7 of 8, No. 4 ]
Value-at-risk measures for the PSE index using hidden markov models
Authors: Joselito C. Magadia
Abstract:
VaR measures for the PSE index are estimated using an m-state normal-hidden Markov model. The estimation procedure will be done under an unconditional approach and a conditional approach. Backtesting will be done to assess how well the estimates performed.
Keywords: homogeneous, irreducible, aperiodic Markov Chain
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Year: 2013 Vol.: 62 No.: 1
Record ID: 36 [ Page 7 of 8, No. 5 ]
Bootstrap estimation of the average household expenditure on personal care and effects of regional level
Authors: Jachelle Anne G. Dimapilis
Abstract:
This study explored the use of bootstrap in estimating households’ expenditure on personal care and effects at regional level. Bootstrap yields superior estimates compared to the estimates of simple random sampling without replacement (SRSWOR). Bootstrap estimates have lower variance than SRSWOR estimates. Bootstrap estimates have smaller percentage difference from the actual mean compared with SRSWOR estimates.
Keywords: survey sampling; predictive estimation; bootstrap estimation
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Year: 2013 Vol.: 62 No.: 1
Record ID: 35 [ Page 7 of 8, No. 6 ]
Nonparametric transfer function model with localized temporal effect
Authors: John Carlo P. Daquis
Abstract:
A semiparametric transfer function model is proposed and estimated using the backfitting algorithm. Simulation studies indicated that the procedure provides robust estimates for the transfer function especially for short time series data. This provides a viable alternative to the parametric transfer function model that requires large number of time points to estimate a number of parameters of the model. Furthermore, in the presence of seasonality or structural change, the procedure generally yields more robust estimates of the transfer function model than the maximum likelihood estimates of the parameters of the parametric model.
Keywords: transfer function model; semiparametric model; backfitting; mixed models
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Year: 2013 Vol.: 62 No.: 1
Record ID: 34 [ Page 7 of 8, No. 7 ]
Sampling from a Skewed Population: The Sampling Design of the 2011 Survey of Enterprises in the Philippines
Authors: Erniel B. Barrios
Abstract:
Keywords:
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Year: 2012 Vol.: 61 No.: 2
Record ID: 33 [ Page 7 of 8, No. 8 ]
Robust Methods in Time Series Models with Volatility
Authors: Wendell Q. Campano
Abstract:
Volatility in time series data is often accounted into the model by postulating a conditionally heteroskedastic variance. In-sample prediction maybe satisfactory but the out-sample prediction is usually problematic. A test for presence of volatility through a nonparametric method is proposed. An estimation procedure for the stationary part of the model by integrating block bootstrap and AR-sieve into the forward search algorithm is also provided. Simulation studies indicated high power for the nonparametric procedure in detecting local volatilities. On the other hand, the estimation method generated robust estimates of the parameters of the time series model in the presence of temporary volatility.
Keywords: block bootstrap; AR-sieve; forward search algorithm; nonparametric test; volatility
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Year: 2012 Vol.: 61 No.: 2
Record ID: 32 [ Page 7 of 8, No. 9 ]
Poisson Spatial Autoregression Modelling of Poverty Count Data in the Philippines
Authors: John Erwin S. Banez
Abstract:
Count data with skewed distribution and possible spatial autoregression (SAR) often causes difficulty in modelling. Violations on the assumptions in ordinary least squares (OLS) may occur. While Poisson regression can offer some remedy in modelling count data, it still does not take into account the spatial dependencies of the data. This paper uses general linear estimation via backfitting algorithm in Poisson-SAR of poverty count in the Philippines for 2000. The model is assessed based on comparison from other models and the actual poverty count (MAPE and poverty map). MAPE was lowest in Poisson-SAR compared to other models.
Keywords: spatial autoregression; backfitting algorithm; poisson regression
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Year: 2012 Vol.: 61 No.: 2
Record ID: 31 [ Page 7 of 8, No. 10 ]
Nonparametric Bootstrap Estimation of the Population Ratio Using Ranked Set Sampling
Authors: Kevin Carl P. Santos; Charisse Mae I. Castillo; Reyna Belle d.S. de Jesus; Nina B. Telan; Crystal Angela P. Vidal
Abstract:
Ranked Set Sampling (RSS) yields unbiased and more reliable estimators of the population mean and proportion while keeping low costs. Using nonparametric bootstrap estimation, the efficiency of the ratio estimates using RSS with Simple Random Sampling (SRS) are compared. A simulation study accounting for the sampling rate, population size, population variance and correlation with the concomitant variable was conducted to compare RSS and SRS in estimating ratios. When ranking was done on the numerator characteristic, RSS generally performs better than SRS in terms of their relative bias. Likewise, in terms of precision, RSS generally produces better estimates when ranking was done on the numerator characteristic. On homogeneous populations, contrary to what was expected, RSS performed better over SRS. On heterogeneous populations, on the other hand, the two sampling designs are generally comparable
Keywords: population ratio; ranked set sampling; simple random sampling; nonparametric bootstrap estimation
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Year: 2012 Vol.: 61 No.: 2
Record ID: 30 [ Page 7 of 8, No. 11 ]
Analysis of Mother's Day Celebration Via Circular Statistics
Authors: Ali H. Abuzaid
Abstract:
This paper handles with much emphasis mother's day celebration around the world a day that is celebrated on various days in different countries. These days are marked in relation to certain historical, religious or mythical events for every nation. The celebration of mother's day by 152 nations is analyzed using a set of circular statistics procedures to study its characteristics. The frequencies of celebration days are modeled, possible clusters and outliers are defined to assess possible factors that may affect the celebration in a certain date. These factors are found to be culture, language, colonization and neighborhood with insignificant role of religion.
Keywords: boxplot; cluster; direction; outlier
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Year: 2012 Vol.: 61 No.: 2
Record ID: 29 [ Page 7 of 8, No. 12 ]
Purposive Sampling as an Optimal Bayes Sampling Design
Authors: Jacqueline M. Guarte
Abstract:
Purposive sampling takes place when the researcher’s knowledge about the population is used to handpick the units to be included in the sample. This is hinged on the experienced researcher’s belief that the handpicked sampling units will provide “enough” information to characterize the population. Bayesian analysis makes explicit use of prior information as part of the model to satisfy some optimality criteria. Hence, purposive rather than purely random locations of design points need to be chosen. This paper presents a proof that purposive sampling is an optimal Bayes sampling design. Purposive sampling satisfies the sufficient condition for an optimal Bayes sampling design set by Zacks (1969) for single-phase designs. It is shown that the posterior Bayes risk of the population parameter ? given the sample observations is independent of the observed values under purposive sampling. The parameter of interest is the population mean. The normal distribution is used for the sampling distribution and the prior distribution of the population mean due to its universal significance and mathematical maneuverability. The squared error loss function is used in determining the posterior Bayes risk associated with estimating the population mean, with the sample mean as estimator. The posterior Bayes risk under simple random sampling is also determined for comparison purposes. It is shown that the risk levels under purposive sampling are lower than those under simple random sampling when important model parameters are made to vary.
Keywords: purposive sampling; optimal Bayes sampling design; posterior Bayes risk
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Year: 2012 Vol.: 61 No.: 2
Record ID: 28 [ Page 7 of 8, No. 13 ]
Small Area Estimation with a Multivariate Spatial-Temporal Model
Authors: Arturo M. Martinez, Jr
Abstract:
A multivariate generalization of a spatial-temporal is postulated and used in model-based small area estimation where small area information is borrowed from other units through spatial and temporal correlations. An estimation procedure that combined the backfitting algorithm, AR-sieve bootstrap and Lorenz curve parameterization is proposed. The procedure is illustrated using data on mean per capita income quintiles of households in the Philippines with provincial unit of analysis. The generation of unit-record synthetic household income is feasible even if modeling is done at the provincial level. Estimates of poverty indices based on the synthetic unit-record data generated from the multivariate spatial-temporal model are more reliable than the direct survey estimates. There are only small deviations between the model-based and direct survey estimates of poverty indices at the domain level that validates the accuracy of the model-based small area estimates generated from the multivariate spatial-temporal model.
Keywords: backfitting algorithm; AR-sieve bootstrap; Lorenz curve parameterization; poverty index
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Year: 2012 Vol.: 61 No.: 2
Record ID: 26 [ Page 7 of 8, No. 14 ]
On the Misuse of Slovin's Formula
Authors: Jeffry J. Tejada; Joyce Raymond B. Punzalan
Abstract:
In a number of research studies involving surveys, the so-called Slovin's formula is used to determine the sample size. Unfortunately, many of these studies use the formula inappropriately, giving the wrong impression that it can be used in just about any sampling problem. This paper provides a careful examination of the formula, showing that it is applicable only when estimating a population proportion and when the confidence coefficient is 95%. Moreover, it is optimal only when the unknown population proportion is believed to be close to 0.5.
Keywords: Slovin�s formula; sample size; margin of error
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Year: 2012 Vol.: 61 No.: 1
Record ID: 25 [ Page 7 of 8, No. 15 ]
Ranked Set Sampling
Authors: Kevin Carl P. Santos
Abstract:
Keywords:
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Year: 2012 Vol.: 61 No.: 1
Record ID: 24 [ Page 7 of 8, No. 16 ]
A Multivariate Probit Analysis on the Factors Influencing the Adoption of Water Saving Technologies by Rice Farmers in Sto. Domingo, Nueva Ecija
Authors: Daniel R. Raguindin; Eiffel A. De Vera
Abstract:
We study the adoption of rice farmers of some water saving technologies (WST) such as controlled irrigation, direct seeding, land leveling and aerobic rice system. A multivariate probit model for the adoption of each WST is constructed since usage of different technologies exhibit correlation. The significant factors that influence the WST adoption are education, experience in rice farming, family income of the farmers, and size of manpower involved in farming. Higher education is needed to enhance the ability to successfully implement the WST. Experience in rice farming, i.e., the number of years a farmer is involved in rice management and production, increases the likelihood of adoption among farmers. Farmers with high income have lower likelihood of adoption since the production system in place is already efficient. Furthermore, a farmer is more likely to adopt the technology as more manpower is involved in the production system. The estimated model indicated that the probability of adoption of controlled irrigation is higher than the other three WST. In addition, the adopters of WST had greater output in terms of the harvested rice.
Keywords: water saving technology; multivariate probit model; univariate probit model
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Year: 2012 Vol.: 61 No.: 1
Record ID: 23 [ Page 7 of 8, No. 17 ]
Sampling with Probability Proportional to Aggregate Size Using Nonparametric Bootstrap in Estimating Total Production Area of Top Cereals and Root Crops Across Philippine Regions
Authors: Maria Sofia A. Poblador; Iris Ivy M. Gauran
Abstract:
Cereal and root crop production are of primary interest to the country’s agricultural industry. The need to obtain reliable estimates of total area of production is therefore crucial. This paper examines the Sampling with Probability Proportional to Aggregate Size (PPAS) in terms of unbiasedness and precision of estimates as compared to two known sampling designs, Simple Random Sampling without Replacement (SRSWOR) and Sampling with Probability Proportional to Size Without Replacement (PPSWOR). Among several crops included in the 2002 Philippine Census of Agriculture, rice and corn are considered for cereals, while cassava and sweet potato for root crops. Crop area, which is believed to be highly correlated with total production area, is utilized as auxiliary information. Estimates of total production area are obtained under 1%, 5% and 10% sampling rates. To be able to evaluate precision of PPAS estimates, nonparametric bootstrap variance estimation is performed. It was found out that PPAS estimates are generally better than the two other sampling designs when it comes to precision but almost at par when it comes to unbiasedness.
Keywords: probability proportional to aggregate size sampling; probability proportional to size sampling; simple random sampling; nonparametric bootstrap estimation
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Year: 2012 Vol.: 61 No.: 1
Record ID: 22 [ Page 7 of 8, No. 18 ]
Econometric Modeling of Panel Data on the Saving Patterns of Philippine Agricultural Households
Authors: Angelo M. Alberto; Lisa Grace S. Bersales
Abstract:
This study aims to identify significant determinants of Philippine agricultural household saving using aggregate (regional) household panel data from the Family Income and Expenditure Survey (FIES) (1991 to 2006). Two definitions of saving are used - with and without expenses on durable goods as expenditure item. Guided by analyses using fixed effects models for panel data, the study identifies age of household head, self-employment of household head, land distribution, and young dependency rate as significant determinants of agricultural household saving. Self-employment, however, is significant only when expenses on durable goods is considered as an expenditure item. Also, time and cross-section fixed effects suggest that there are certain years and regions which had less agricultural household saving.
Keywords: panel data; fixed effects; saving rate; agricultural household
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Year: 2012 Vol.: 61 No.: 1
Record ID: 21 [ Page 7 of 8, No. 19 ]
Classification of Congenital Hypothyroidism in Newborn Screening Using Self-Organizing Maps
Authors: Iris Ivy M. Gauran; Maria Sofia Criselda A. Poblador
Abstract:
Each day, 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 widely known as a tool for visualization and clustering, the researchers want to probe on its ability as a tool for classification, particularly in detecting outliers. Results show that a lower misclassification rate yields from a self-organizing map with higher learning rate and larger training sample size. A bootstrap estimate of the variability of the misclassification error of roughly around 5% is also obtained. The misclassification error rate is lower when the original validation sample is used, compared to the average misclassification error rate computed from the bootstrap validation samples. Particularly, for a learning rate of 0.8 and a ratio of 2:1 training to validation sample, a 2.04% misclassification against 7.93% misclassification with 4.86% standard deviation is observed.
Keywords: self-organizing kohonen maps (SOM); classification algorithm; outlier detection; newborn screening for congenital hypothyroidism
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Year: 2012 Vol.: 61 No.: 1
Record ID: 20 [ Page 7 of 8, No. 20 ]
In a number of research studies involving surveys, the so-called Slovin's formula is used to determine the sample size. Unfortunately, many of these studies use the formula inappropriately, giving the wrong impression that it can be used in just about an
Authors: Lara Paul D. Abitona; Zita VJ Albacea
Abstract:
This paper aims to present methodologies in estimating the number of Vitamin A defi cient children aged six months to fi ve years in the Philippine provinces. Data from the 6th National Nutrition Survey (NNS), specifi cally, the data on plasma retinol which is used to directly determine Vitamin A defi ciency is used to compare direct and model-based methods. The direct estimates obtained was used as the dependent variable while the 2000 Census of Population and Housing and 2002 Field Health Service Information System were used as sources of auxiliary variables in the Poisson regression fi tted using robust standard errors which resulted to a model with Pseudo-R2 of 55.57%. Measures of precision and reliability were also obtained to assess the properties of the estimates for the provincial estimates. In direct estimation technique, 71 provinces have valid estimates but the coeffi cient of variations are all greater than 20%. On the other hand, valid model-based estimates using Poisson regression were observed for 72 provinces, but the coeffi cient of variations are at most 10% for 78% of these provinces. The use of Poisson regression based model generated more precise estimates of the number of children with Vitamin A defi ciency for the provinces.
Keywords: Small area estimation; Poisson regression model
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Year: 2012 Vol.: 61 No.: 1