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Record ID: 19 [ Page 15 of 16, No. 1 ]
Assessing Strength of Seasonality Through Sample Entropy: A Simulation Study
Authors: John Carlo P. Daquis; Maria Lizeth M. Laus; Nikki E. Supnet
Abstract:
This paper investigates the behaviour of sample entropy when used as a measure of seasonality of time series. Sample entropy decreases when the series becomes less complex or when regular patterns emerge. The more regular patterns in seasonal data compared to those of non-seasonal data is used in providing evidence that sample entropy is inversely related to the likelihood that seasonality exists in the data. A simulation study was conducted to assess the behaviour of the sample entropy in relation to seasonality. Sample entropy yields large values for time series without seasonality, and as the extent of seasonality becomes dominant, the value decreases. The sample entropy becomes a more reliable measure of seasonality as the length of the time series increases.
Keywords: entropy; sample entropy; seasonality; time series
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Year: 2012 Vol.: 61 No.: 1
Record ID: 27 [ Page 15 of 16, No. 2 ]
Statistical Models for Extreme Values
Authors: Peter Julian A. Cayton
Abstract:
Keywords:
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Year: 2012 Vol.: 61 No.: 1
Record ID: 18 [ Page 15 of 16, No. 3 ]
Sample Sizes to Compare Two Poisson Rates
Authors: Edsel A. Pena
Abstract:
In this note, procedures for determining the sample sizes needed to compare the rates of two Poisson populations to achieve a pre-specified power at a given ratio of the rates are proposed. The first method relies on a conditional uniformly most powerful test (CUMPT) which leads to sample sizes that will guarantee the desired power, but at the cost of using more units than necessary. The second method relies on a normal approximation and may not always guarantee that the desired power will be achieved, but generally yields a power close to the pre-specified value and prescribes smaller sample sizes than the CUMPT-based method. Properties of the procedures are examined using simulation studies. The particular applicability and motivating situations leading to these procedures are in colon cancer research. Illustrations of the applicability of the procedures in studies dealing with tumor counts in mice are presented.
Keywords: conditional uniformly most powerful test; normal approximation test; power function; test function.
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Year: 2012 Vol.: 61 No.: 1
Record ID: 17 [ Page 15 of 16, No. 4 ]
Bootstrap Methods
Authors: Erniel B. Barrios
Abstract:
Keywords:
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Year: 2011 Vol.: 60 No.: 1
Record ID: 16 [ Page 15 of 16, No. 5 ]
A Dose of Business Intelligence: Data Mining
Authors: Joseph Ryan G. Lansangan,
Abstract:
Keywords:
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Year: 2011 Vol.: 60 No.: 1
Record ID: 15 [ Page 15 of 16, No. 6 ]
Copula-Based Vector Autoregressive Models for Bivariate Cointegrated Data
Authors: Hideaki Taima; Ana Maria L. Tabunda,
Abstract:
The copula method is well applied in finance and actuarial science but its application in economic studies is limited and its use in the cointegration framework virtually nil. This paper explores the use of copula method to analyze the remaining dependence after a cointegration relationship is modeled. Specifically, simulated data is used to characterize the behavior of the dependence parameter estimates of several copulas fitted to the distribution of the residuals after cointegrated Vector Autoregressive (VAR) and Vector Error-Correction Mechanism (VECM) models are fitted, as well as evaluate the forecasting ability of the copula-based models. The Clayton, Frank, Gaussian, Gumbel and Plackett copulas are used and are compared on the basis of bias, root mean square error (RMSE) and maximum likelihood. The density forecasting ability of the copula-based VAR and VECM is then compared with that of standard models via conditional Kullback-Leibler Information Criterion (KLIC) divergence measure using simulated and empirical data. The simulation results indicate that the copula-based models generally have better density forecasting ability than standard VAR and VECM models, a finding that is supported in the application of a copula-based VAR to empirical data.
Keywords: Copula; Cointegration; VAR; VECM
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Year: 2011 Vol.: 60 No.: 1
Record ID: 14 [ Page 15 of 16, No. 7 ]
Nearest-Integer Response from Normally-Distributed Opinion (NIRNDO) Model for Likert Scale
Authors: Jonny B. Pornel, Vicente T. Balinas, Giabelle A. Saldaa
Abstract:
This paper proposes that respondents’ opinions on Likert Scale items are normally distributed around their latent ability although their observable responses will be integers in the scale nearest to those opinions. This paper tested the appropriateness of the model on actual data gathered by a Likert scale developed to measure attitude of teachers towards research undertaking. The paper then proceeded to test the soundness of common research practice of using mean and standard deviation to estimate the respondents’ latent ability. The results show that the NIRNDO model could be used appropriately to model responses on Likert scale. Also, the results show that using the mean response to a Likert scale, the resulting 95% confidence interval (mean + 1.96 SEM) would be effective at least 90% of the time. This effectiveness is guaranteed for latent ability in the optimum range [u+0.8, v-0.8] where u and v is the lowest and highest points of the scale.
Keywords: Likert Scale; NIRNDO Model; latent ability
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Year: 2011 Vol.: 60 No.: 1
Record ID: 13 [ Page 15 of 16, No. 8 ]
Substance Use Among Serious Adolescent Offenders Following Different Patterns of Antisocial Activity
Authors: Michelle Besana; Edward P. Mulvey
Abstract:
The present study examines individual differences in the levels of substance use in a sample (n=1,067) of male serious adolescent offenders following distinct trajectories of criminal offending over a three (3) year period. The levels of substance use are compared for the different offender groups controlling the effects of age, ethnicity, and diagnosis of previous drug and alcohol abuse/dependence. The association between antisocial activity and the level of substance use was also examined and compared for the different groups after controlling the effect of institutional placement. The growth or decline in substance use was investigated and compared for the different groups above and beyond the effects of antisocial activity and institutional confinement. After fitting a series of hierarchical generalized linear models for repeated measurements data, results revealed that significant differences in the level of substance use exist among the different offender groups in the sample. Antisocial activity is associated with the level of substance use over time after controlling the effect of institutional placement in all offender groups. Above and beyond the effect of antisocial activity and institutional placement, substance use is increasing over the data collection period in all groups, but the rate of growth is highest in the lowest offending group.
Keywords: hierarchical generalized linear models; growth curve models; substance use; antisocial activity; delinquency; serious adolescent offenders
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Year: 2011 Vol.: 60 No.: 1
Record ID: 12 [ Page 15 of 16, No. 9 ]
Food Inflation, Underemployment and Hunger Incidence: A Vector Autoregressive (VAR) Analysis
Authors: Dennis S. Mapa; Fatima C. Han; Kristine Claire O. Estrada
Abstract:
The high level of hunger incidence in the country is perhaps one of the most pressing issues that need to be addressed by our policy makers. Official government statistics and data from self-rated hunger surveys show an increasing trend in hunger incidence among Filipino households. Data from National Statistical Coordination Board (NSCB) show that the percentage of Filipinos experiencing hunger almost remained the same, decreasing only slightly from 11.1 percent in 2003 to 10.8 percent in 2009. The Social Weather Stations (SWS) quarterly surveys on hunger incidence also show an increasing trend in the percentage of families that experienced hunger, reaching an alarming level of 24 percent in December 2009, representing about 4.4 million households. One probable cause of the increasing trend in hunger is the rising food prices akin to what the country experienced in 2008. This paper aims to determine the impact of food inflation and underemployment on hunger incidence in the Philippines, using the hunger incidence data from the SWS. A vector autoregressive (VAR) model is used to determine the effect of a shock or increase to food inflation and underemployment on total involuntary hunger. Results show that an increase in food prices at the current quarter will increase hunger incidence for five quarters. Shocks to underemployment will also increase hunger incidence but the effects last for only two quarters. The results of this study provide relevant information that will be useful in crafting policies related to the Hunger Mitigation Program of the government.
Keywords: hunger; food inflation; underemployment; vector autoregressive
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Year: 2011 Vol.: 60 No.: 1
Record ID: 11 [ Page 15 of 16, No. 10 ]
Length of a Time Series for Seasonal Adjustment: Some Empirical Experiments
Authors: Lisa Grace S. Bersales
Abstract:
Use of 5 to 15 years of quarterly or monthly data is suggested when doing seasonal adjustment using X11 and its variants. This is meant to address changes in the structure of the time series. Philippine time series are good candidates for this practice since they usually exhibit frequent changes in patterns. Empirical validation of the suggested length of series is done for seasonal ARMA processes. Different quarterly series were simulated for the following situations and seasonal adjustment was done for various lengths of time series: (1) processes without any structural change; (2) processes with abrupt permanent change in structure; (3) processes with gradual permanent change in structure. For all types of processes, both weak and strong seasonality were considered. Regression models were used in testing the effect of length of series used in seasonal adjustment to the error in estimating the seasonal factor. Results show that the length of series used does not have significant effect on the seasonal adjustment for processes without structural change and with abrupt permanent structural change. On the other hand, for processes with gradual permanent change, use of longer lengths of series for seasonal adjustment is better.
Keywords: seasonal adjustment, seasonal factor, X11-ARIMA, seasonal ARMA processes
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Year: 2011 Vol.: 60 No.: 1