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Record ID: 100 [ Page 7 of 16, No. 1 ]
Zero-Truncated New Quasi Poisson-Lindley Distribution and its Applications
Authors: Rama Shanker and Kamlesh Kumar Shukla
Abstract:
A zero-truncated new quasi Poisson-Lindley distribution (ZTNQPLD), which includes zero-truncated Poisson-Lindley distribution (ZTPLD) as a particular case, has been studied. Its probability mass function has also been obtained by compounding size-biased Poisson distribution (SBPD) with an assumed continuous distribution. The rth factorial moment of ZTNQPLD have been derived and hence its raw moments and central moments have been presented. The expressions for coefficient of variation, skewness, kurtosis, and index of dispersion have been given and their nature and behavior have been studied graphically. The method of maximum likelihood estimation has been discussed for estimating the parameters of ZTNQPLD. Finally, the goodness of fit of ZTNQPLD has been discussed with some datasets and the fit has been found better as compared with zero truncated Poisson distribution (ZTPD) and zero- truncated Poisson- Lindley distribution (ZTPLD).
Keywords: Zero-truncated distribution, New quasi Poisson-Lindley distribution, compounding, moments, Maximum Likelihood estimation, Goodness of fit.
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Year: 2017 Vol.: 66 No.: 2
Record ID: 99 [ Page 7 of 16, No. 2 ]
Regression and Variable Selection via A Layered Elastic Net
Authors: Michael Van B. Supranes and Joseph Ryan G. Lansangan
Abstract:
One approach in modeling high dimensional data is to apply an elastic net (EN) regularization framework. EN has the good properties of least absolute shrinkage selection operator (LASSO), however, EN tends to keep variables that are strongly correlated to the response, and may result to undesirable grouping effect. The Layered Elastic Net Selection (LENS) is proposed as an alternative framework of utilizing EN such that interrelatedness and groupings of predictors are explicitly considered in the optimization and/or variable selection. Assuming groups are available, LENS applies the EN framework group-wise in a sequential manner. Based on the simulation study, LENS may result to an ideal selection behavior, and may exhibit a more appropriate grouping effect than the usual EN. LENS results to poor prediction accuracy, but applying OLS on the selected variables may yield optimum results. At optimal conditions, the mean squared prediction error of OLS on LENS-selected variables are on par with the mean squared prediction error of OLS on EN-selected variables. Overall, applying OLS on LENS-selected variables makes a better compromise between prediction accuracy and ideal grouping effect.
Keywords: regression, variable selection, variable clustering, high dimensional data, elastic net, grouping effect
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Year: 2017 Vol.: 66 No.: 2
Record ID: 98 [ Page 7 of 16, No. 3 ]
Asymptotic Decorrelation of Discrete Wavelet Packet Transform of Generalized Long-memory Stochastic Volatility
Authors: Alex C. Gonzaga
Abstract:
We derive the asymptotic properties of discrete wavelet packet transform (DWPT) of generalized long-memory stochastic volatility (GLMSV) model, a relatively general model of stochastic volatility that accounts for persistent (or long-memory) and seasonal (or cyclic) behavior at several frequencies. We derive the rates of convergence to zero of between-scale and within-scale wavelet packet coefficients at different subbands. Wavelet packet coefficients in the same subband can be shown to be approximately uncorrelated by appropriate choice of basis vectors using a white noise test. These results may be used to simplify the variance-covariance matrix into a diagonalized matrix, whose diagonal elements have the least distinct variances to compute.
Keywords: discrete wavelet packet transform, generalized longmemory stochastic volatility, asymptotic decorrelation
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Year: 2017 Vol.: 66 No.: 2
Record ID: 96 [ Page 7 of 16, No. 4 ]
Survival Analysis for Weaning Time of the Palestinian Children
Authors: Ali H. Abuzaid and Raida F. Zaqout
Abstract:
This study addresses the factors that have an effect on the weaning time of the Palestinian children based on the Palestinian family survey data in 2006. It was found that the Weibull parametric model is the most appropriate one to fit the data. The study showed that factors such as child’s weight at birth, child’s age, mother’s age at delivery, and mother’s educational status have significant effects on the weaning time. The findings also revealed that factors such as mother’s refugee status, locality type, total live births, and mother’s smoking status do not have any significant effect at 0.05 level of significance on the duration of breastfeeding.
Keywords: breastfeeding, censored, Cox proportional model, Wald statistic
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Year: 2017 Vol.: 66 No.: 1
Record ID: 95 [ Page 7 of 16, No. 5 ]
Modeling Iloilo River Water Quality
Authors: Michelle B. Besana and Philip Ian P. Padilla
Abstract:
The analysis of covariance model (ANCOVA) with heterogeneous variance first-order autoregressive error covariance structure (ARH1) was used to model the differences in fecal streptococci concentration in Iloilo River over time with fixed site and seasonal effects as primary factors of interest, and water temperature, pH, dissolved oxygen, and salinity as covariates. The restricted maximum likelihood estimation (REML) procedure was used to derive the parameter estimates and the Kenward-Roger adjustment in the degrees of freedom was used to better approximate the distributions of the test statistics. The effect of season was highly significant (p = 0.0019). The site effect was significant at the 0.0539 level. The effects of water surface temperature and pH were significant at the 0.0655 and 0.0828 level, respectively. The effects of dissolved oxygen and salinity were not significant. Although the coefficient of determination was modest, the result of the study is useful in characterizing the dynamics of Iloilo River bacteriological system which contributes to an improved understanding of the Iloilo River water quality.
Keywords: analysis of covariance (ANCOVA), heterogeneous variance first-order autoregressive error covariance structure (ARH1), restricted maximum likelihood estimation (REML), fecal indicator bacteria (FIB), fecal streptococcus
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Year: 2017 Vol.: 66 No.: 1
Record ID: 94 [ Page 7 of 16, No. 6 ]
An Index of Financial Inclusion in the Philippines: Construction and Analysis
Authors: Mynard Bryan R. Mojica and Claire Dennis S. Mapa
Abstract:
Financial inclusion has become a policy priority in many developing countries, including the Philippines. However, the issue of its robust measurement is still outstanding. The challenge comes from the fact that financial inclusion is a multidimensional phenomenon. A comprehensive measure is therefore needed to adequately gauge the inclusiveness of a financial system. This paper constructed a Financial Inclusion Index (FII) to measure access to and usage of financial services in the Philippines using provincial data. Results show that while there are marked geographical disparities based on the FII, there is significant positive spatial autocorrelation indicating that nearby provinces exhibit similar levels of financial inclusion. The paper also showed the relationship between the FII and some variables that are often linked to financial inclusion such as income, poverty, literacy, and employment as well the province’s level of human development and competitiveness. On the methodological side, possible improvements and technical innovations in constructing the FII are laid out to maximize its potential as an analytical tool for surveillance and policy-making.
Keywords: inclusive finance, composite indicator, financial inclusion index
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Year: 2017 Vol.: 66 No.: 1
Record ID: 93 [ Page 7 of 16, No. 7 ]
Comparison of Regression Estimator and Ratio Estimator: A Simulation Study
Authors: Dixi M. Paglinawan
Abstract:
We compared ratio and regression estimators empirically based on bias and coefficient of variation. Simulation studies accounting for sampling rate, population size, heterogeneity of the auxiliary variable x, deviation from linearity and model misspecification were conducted. The study shows that ratio estimator is better than regression estimators when regression line is close to the origin. Ratio and regression estimators still work even if there is a weak linear relationship between x and y, provided that there is minimal, if not absent, model misspecification. When the relationship between the target variable and the auxiliary variable is very weak, bootstrap estimates yield lower bias. Regression estimator is generally more efficient than ratio estimator.
Keywords: auxiliary variable, ratio estimator, regression estimator, bootstrap estimator
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Year: 2017 Vol.: 66 No.: 1
Record ID: 92 [ Page 7 of 16, No. 8 ]
A Class of Ratio-Cum-Product Type Exponential Estimators under Simple Random Sampling
Authors: Gajendra K. Vishwakarma and Sayed Mohammed Zeeshan
Abstract:
In this paper, a class of ratio-cum-product type exponential estimators have been proposed under simple random sampling to estimate the population mean. The proposed class of estimators has been compared with the other existing estimators. We have compared the efficiency of the proposed class of estimators with the other standard estimators and found through empirical study that the previous estimators are inferior to the present proposed class of estimators. The population used in the empirical study are all varying very much from each other and thus it demonstrate the superiority of the present estimators under all type of situation.
Keywords: auxiliary variable, study variable, simple random sampling, ratio type estimators, product type estimators, bias, MSE
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Year: 2017 Vol.: 66 No.: 1
Record ID: 91 [ Page 7 of 16, No. 9 ]
An Exponentially Weighted Moving Average Control Chart for Zero-Truncated Poisson Processes: A Design and Analytic Framework with Fast Initial Response Feature
Authors: Robert Neil F. Leong, Frumencio F. Co, Vio Jianu C. Mojica and Daniel Stanley Y. Tan
Abstract:
Inspired by the capability of exponentially-weighted moving average (EWMA) charts to balance sensitivity and false alarm rates, we propose one for zero-truncated Poisson processes. We present a systematic design and analytic framework for implementation. Further, we add a fast initial response (FIR) feature which ideally increases sensitivity without compromising false alarm rates. The proposed charts (basic and with FIR feature) were evaluated based on both in-control average run length (ARL0) to measure false alarm rate and out-of-control average run length (ARL1) to measure sensitivity to detect unwanted shifts. The evaluation process used a Markov chain intensive simulation study at different settings for different weighting parameters (ω). Empirical results suggest that for both scenarios, the basic chart had: (1) exponentially increasing ARLs as a function of the chart threshold L; and (2) ARLs were longer for smaller ωs. Moreover, the added FIR feature has indeed improved ARL1 within the range of 5% - 55%, resulting to quicker shift detections at a relatively minimal loss in ARL0. These results were also compared to Shewhart and CUSUM control charts at similar settings, and it was observed that the EWMA charts generally performed better by striking a balance between higher ARL0 and lower ARL1. These advantages of the EWMA charts were more pronounced when larger shifts in the parameter λ happened. Finally, a case application in monitoring hospital surgical out-of-controls is presented to demonstrate its usability in a real-world setting.
Keywords: exponentially-weighted moving average control chart, zero-truncated Poisson process, fast initial response feature, average run length, infection control
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Year: 2017 Vol.: 66 No.: 1
Record ID: 90 [ Page 7 of 16, No. 10 ]
Spatial-Temporal Models and Computational Statistics Methods: A Survey
Authors: Erniel B. Barrios and Kevin Carl P. Santos
Abstract:
We introduce panel models and identify its link to spatial-temporal models. Both models are characterized and differentiated through the variance-covariance matrix of the disturbance term. The resulting estimates or tests are as complicated as the nature of the said variance-covariance matrix. Some iterative methods typically used in computational statistics are also presented. These methods are used in conducting statistical inference for spatial-temporal models.
Keywords: panel data, spatial-temporal model, forward search algorithm, additive models, backfitting algorithm, isotonic regression
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Year: 2017 Vol.: 66 No.: 1