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Record ID: 120 [ Page 5 of 16, No. 1 ]
Rapid Assessment of Real Estate Loan Disapproval via Predictive Modeling: A Case for the Philippines
Authors: Adrian Nicholas A. Corpuz and Joseph Ryan G. Lansangan
Abstract:
The Philippines is currently experiencing a housing backlog and is expected to reach 6.5 million by the year 2030 if nothing is done about this. It is in this context where the government and the private sector have partnered themselves to address the backlog. Financing institutions such as private banks and the Home Development Mutual Fund (i.e. Pag-IBIG) offer different home loans for Filipinos to be able to afford these houses. Using a local real estate development’s dataset, the study explores the application of predictive models in quickly determining whether a client will likely be able to get a home loan approved or not once he or she submits the preliminary documents for a home loan. Results show that in terms of accuracy, decision trees and random forest are superior in predicting home loan disapproval than binary logistic regression. The best predictive model is the random forest model, and results show that the main determinants of getting a home loan approved are loan equity term, total contract price of the house, equity payment status, and the income of the client.
Keywords: real estate, home loan, binomial logistic regression, decision tree, CART, CTree, CHAID, random forest
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Year: 2019 Vol.: 68 No.: 2
Record ID: 119 [ Page 5 of 16, No. 2 ]
Spatio-temporal Analysis of Animal Rabies Cases in Negros Occidental, Philippines from 2012 to 2018
Authors: Joseph L. Arbizo, Philip Ian P. Padilla, Marilyn S. Sumayo, Mitzi N. Meracap, Andrea Marie N. Napulan, Rex Victor V. Dolorosa, Princess Monic Q. Velasco, Leslie S. Asorio, Thea Joy A. Clarito, James Matthew V. Recabar, Sael D. Rodriguez
Abstract:
Rabies is a dangerous and deadly zoonotic disease that infects domestic and wild animals and is transmissible to humans. Animal rabies, particularly of canine and feline type, is considered to be a serious threat to public health. Thus, all prevention and control efforts to reduce the cases of human rabies are stemming from the identification of high-risk communities where presence of canine or feline rabies cases are prevalent. Having recorded the highest number of cases in recent years, this research utilized the spatiotemporal analysis of animal rabies cases in Negros Occidental, Western Visayas, Philippines. The hotspot analysis was based on Getis-Ord-Gi* statistic to estimate statistically significant hotspots of animal rabies cases in the province. Mean center and standard deviational ellipse were performed to identify the epicenter, dispersion, and yearly directional trends of animal rabies cases. The emerging hotspot analysis based on the Getis-Ord-Gi* and Mann- Kendall statistics was performed to identify statistically significant clusters with significant temporal trend. Spatial analysis identified the major cities such as Bacolod City and Bago City and their surrounding cities and municipalities to be of high risk to animal rabies cases from 2012 to 2018. The epicenter of cases is slowly shifting from the northern part in earlier years towards the central part of the province in recent years. Twenty-six (26) space-time clusters of animal rabies cases in Negros Occidental were found to have “intensifyingâ€, “consecutiveâ€, “oscillatingâ€, and “sporadic†time trends. Two clusters classified as “new†hotspots were identified in the central part of the province. Results presented in this study could be of service for rabies cases surveillance, and in developing care and prevention programs for rabies control.
Keywords: animal rabies, spatio-temporal analysis, zoonosis, rhabodoviruses
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Year: 2019 Vol.: 68 No.: 2
Record ID: 118 [ Page 5 of 16, No. 3 ]
Influence of Physicochemical Water Parameters on the Total Weight of the Slipper-shaped Oyster Crassostrea iredalei in Visayas, Philippines
Authors: Michelle B. Besana, Ma. Ramela Angela C. Bermeo, and Philip Ian P. Padilla
Abstract:
Annual assessments of the total weights of the most abundant oyster species in the Philippines, Crassostrea iredalei, for two consecutive years were examined across ten different sampling sites in Visayas, Philippines. The ANCOVA model was used to investigate the effects of the different sampling sites and the different physicochemical water parameters as covariates on the log total weight. The ANOVA model was also used to examine site differences in the log total weight without taking into account the effects of the covariates. ANOVA and ANCOVA results were compared to distinguish site differences with and without the covariates in the model. The results from the ANOVA model revealed that there were significant differences in the mean log total weight between sites. In the final ANCOVA model, there were still significant differences in the mean log total weight between sites above and beyond the significant positive covariate effect of temperature. The observed variations in the total weight of oysters is most likely due to the varied underlying internal and external factors that affect oyster culture in their respective ecological habitat. The study also reflects both vulnerability and coping mechanism of the Philippine C. iredalei with the variations in temperature which are critical for developing tolerance for positive growth and survival. The findings of this study could promote patterns of selective breeding and culture practices with the additional consideration of environmental factors that would lead to a better understanding of the changing environmental conditions operating in the different culture sites that would help ensure better culture management and harvest.
Keywords: analysis of variance (ANOVA), analysis of covariance (ANCOVA), Tukey-Kramer method, physicochemical water parameter, slipper-shaped oyster Crassostrea iredalei
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Year: 2019 Vol.: 68 No.: 2
Record ID: 117 [ Page 5 of 16, No. 4 ]
Comparison of Official Data Sources and Construction of a Sampling Frame for Household-based Livestock Surveys in Nueva Ecija, Philippines
Authors: Anna Ma. Lourdes S. Latonio, Isidoro P. David, and Zita V.J. Albacea
Abstract:
A basic issue in designing a procedure on selecting a sample is how well the sampling frame corresponds to the target population from where the sample will come from, making the construction of the sampling frame a vital aspect in the development of a sampling design. The main purpose of this paper is to evaluate and compare different official data sources to be able to combine them into a sampling frame appropriate for use in household-based livestock operations surveys for major livestock types (carabao, cattle, goat, and swine) in the Province of Nueva Ecija. The data values of different official data sources available such as the Philippine Carabao Center Inventory (PCCI), Livestock and Poultry Survey (LPS), Barangay Agricultural Profiling Survey (BAPS), and Local Government Unit (LGU) records during 2007-2010 for the province were compared. Scatter plots, strengths of relationships and relative differences between observations on the same variables were analyzed. Specifically, relevant barangay level data common in the different surveys were used to perform the comparisons. Based on the assessment of the different data sources, a barangay level Household Based Livestock Frame (HBLF) was constructed. The constructed HBLF consists of 849 barangays as basic sampling units, each with attached livestock related information, and auxiliary information such as total rice area (TRA) and number of rice growers (NRG). The constructed HBLF can be used in the development of separate or combined sampling designs for household-based livestock surveys in the Province of Nueva Ecija.
Keywords: Agriculture, livestock, sampling, sampling frame, livestock inventory
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Year: 2019 Vol.: 68 No.: 2
Record ID: 116 [ Page 5 of 16, No. 5 ]
Sampling with Probability Proportional to Aggregate Size in Heterogeneous Populations: A Study of Design and Efficiency
Authors: Daniel David M. Pamplona
Abstract:
Sampling with probability proportional to aggregate size (PPAS) is compared with traditional design-unbiased sampling methods under different simulated population scenarios in the estimation of the population total. The study considered both accuracy and precision of the estimates in the comparison. Heterogeneous populations were simulated by exploring varying behaviors of an auxiliary variable and its relationship with the target variable. Results show that the optimality of estimates using PPAS sampling improve as the association between the target variable and auxiliary variable strengthens. Furthermore, PPAS sampling estimates are more stable under large variability in the population.
Keywords: Probability Proportional to Aggregate Size Sampling, Nonparametric Bootstrap, Simple Random Sampling, Probability Proportional to Size Systematic Sampling
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Year: 2019 Vol.: 68 No.: 2
Record ID: 115 [ Page 5 of 16, No. 6 ]
Nonparametric Test of Interaction Effect for 22-Factorial Design with Unequal Replicates: Case of Poisson-Normal Multivariate Data
Authors: Mara Sherlin D. Talento, Marcus Jude P. San Pedro and Erniel B. Barrios
Abstract:
Multivariate Analysis of Variance (MANOVA) is fairly robust to the normality and constant variance assumptions provided that the data is generated from a balanced design. Issues with hypothesistesting arises when error-distribution is non-normal or when the data is generated from an unbalanced design. We propose a nonparametric method of testing interaction effect of the twofactor factorial design with multivariate response and possibly highly unbalanced replicates. Simulation studies indicated that the test is correctly-sized and increasing power with increasing effect-size, and increasing sample size. The parametric test based on MANOVA is incorrectl
Keywords: two-factor factorial design, unbalanced replicates, nonparametric, multivariate data, Poisson and Normal data
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Year: 2019 Vol.: 68 No.: 2
Record ID: 109 [ Page 5 of 16, No. 7 ]
Life in the Fast Food Lane: Understanding the Factors Affecting Fast Food Consumption among Students in the Philippines
Authors: Adina Faye Bondoc, Hannah Felise Florendo, Emilio Jefe Taguiwalo and John Eustaquio
Abstract:
The fast food industry in the Philippines is growing rapidly and is dominating the food service establishments. Together with this influx in fast food establishments is the increase in fast food consumption and an emergence of an unhealthy lifestyle and increase in obesity prevalence, not only among Filipinos, but around the world. The growth of the fast food industry has been aggressive, especially with its advertisements which have been known to target families and the youth. Previous studies have shown that the youth tend to be more affected by fast food obesity than adults. With this, the researchers decided to create a model for whether students eat at fast food chains using the 2011 Global school-based Student Health Survey in the Philippines. Before modelling, factor analysis was performed to bracket variables together. A total of 6 factors arose--namely vices, assistance from others, injuries and bullying, hygiene, active lifestyle, and diet. In modelling using the original variables, various methods were used for variable selection to reduce the forty-seven variables to a manageable number of predictors. These methods were the independent Chi-squared tests, Fisher Exact Tests, Forward and Backward Selection, and Analysis of Deviance. The resulting model showed that some of the most significant predictors for whether or not a student eats fast food is their frequency of drinking soft drinks, eating fruits, and feeling hungry due to lack of food in the house. The weight and sex of a student also significantly affected the response, in which the odds of eating at a fast food chain were for men were 33.84% lower than that of women, and a kilogram increase in a student’s weight increased their odds by 2.5%.
Keywords: fast food, nutrition, factor analysis, logistic regression, Poisson loglinear model, negative binomial loglinear model, Global School-Based Student Health Survey
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Year: 2019 Vol.: 68 No.: 1
Record ID: 108 [ Page 5 of 16, No. 8 ]
Exploring the Disparities on the Actualization of the Ideal Number of Children among Filipino Women
Authors: Patrisha Brynne Agbayani, Kimberly Baltazar, Excel Franco and John Eustaquio
Abstract:
Family size has been consistently associated with poverty incidence as shown by household survey data over time. According to the 2010 Census of Population, the average household size stands at 4.6 members. With this current situation, this research delves deeper into this vital familial attribute by determining the factors that influence the levels of disparity between a woman’s actual and ideal number of children. Furthermore, the research aims to understand how the odds of actualization of the Filipina’s ideal number of children increases or decreases in relation to the factors that were considered in the study.
The regression model for multinomial response utilized in the study is the proportional-odds cumulative logistic regression model. The results of the study have shown that religious affiliation to the Roman Catholic church doubles the estimated odds of exceeding the ideal number of children among women. Meanwhile, the odds of exceeding the desired number of children decrease as financial status improves. Husband-related factors also affect the actualization of the ideal number of children. More importanly, the discrepancy on the woman and husband’s ideal number of children, as well as the experience of emotional abuse from spouse both leads to an increase in the estimated odds of exceeding desired fertility. Lastly, a woman’s use of contraceptives to delay or avoid pregnancy and whether the woman wanted her last pregnancy are also significant factors that affect the actualization of a woman’s ideal number of children.
Keywords: fertility preference, contraceptive behavior, cumulative logit model, multiple response models
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Year: 2019 Vol.: 68 No.: 1
Record ID: 107 [ Page 5 of 16, No. 9 ]
Optimal Variable Subset Selection Problem in Regression Analysis is NP-Complete
Authors: Paolo Victor T. Redondo
Abstract:
Combinatorial and optimization problems are classified into different complexity classes, e.g., whether an algorithm that efficiently solve the problem exists or a hypothesized solution to the problem can be quickly verified. The optimal selection of subset variables in regression analysis is shown to belong to a complexity class called NP-hard (Welch, 1982) in which solutions to the problems in the same class may not be easily (in terms of computing speed) proven optimal. Variable selection in regression analysis based on correlations is shown to be NP-hard, i.e., a complexity class of problems with easily verifiable solutions.
Keywords: optimal variable selection, regression analysis, np-completeness
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Year: 2019 Vol.: 68 No.: 1
Record ID: 106 [ Page 5 of 16, No. 10 ]
Computing the Combined Effect of Measurement Errors and Non-Response using Factor Chain-type Class of Estimator
Authors: Gajendra K. Vishwakarma, Neha Singh and Amod Kumar
Abstract:
In this article, we have suggested an efficient factor chain-type class of estimators in the presence of measurement error and nonresponse simultaneously. It is shown that several estimators can be generated from our proposed class of estimators. Mean Square Error of the proposed class of estimator are derived and compared with other existing estimators. The conditions under which proposed estimator is more efficient are obtained. A theoretical and empirical study has done to demonstrate the efficiency of this estimator over other existing estimators.
Keywords: Auxiliary variable, bias, mean square error, measurement errors, nonresponse, study variable
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Year: 2019 Vol.: 68 No.: 1