Pub Date : 2023-01-01DOI: 10.15446/rce.v46n1.95989
Fernando A. Moala, Gustavo Moraes
In this paper, a set of important objective priors are examined for the Bayesian estimation of the parameters present in the Poisson-Exponential distribution PE. We derived the multivariate Jeffreys prior and the Maximal Data Information Prior. Reference prior and others priors proposed in the literature are also analyzed. We show that the posterior densities resulting from these approaches are proper although the respective priors are improper. Monte Carlo simulations are used to compare the efficiencies and to assess the sensitivity of the choice of the priors, mainly for small sample sizes. This simulation study shows that the mean square error, mean bias and coverage probability of credible intervals under Gamma, Jeffreys' rule and Box & Tiao priors presented equal results, whereas Jeffreys and Reference priors showed the best results. The MDIP prior had a worse performance in all analyzed situations showing not to be indicated for Bayesian analysis of the PE distribution. A real data set is analyzed for illustrative purpose of the Bayesian approaches.
{"title":"Objective Prior Distributions to Estimate the Parameters of the Poisson-Exponential Distribution","authors":"Fernando A. Moala, Gustavo Moraes","doi":"10.15446/rce.v46n1.95989","DOIUrl":"https://doi.org/10.15446/rce.v46n1.95989","url":null,"abstract":"In this paper, a set of important objective priors are examined for the Bayesian estimation of the parameters present in the Poisson-Exponential distribution PE. We derived the multivariate Jeffreys prior and the Maximal Data Information Prior. Reference prior and others priors proposed in the literature are also analyzed. We show that the posterior densities resulting from these approaches are proper although the respective priors are improper. Monte Carlo simulations are used to compare the efficiencies and to assess the sensitivity of the choice of the priors, mainly for small sample sizes. This simulation study shows that the mean square error, mean bias and coverage probability of credible intervals under Gamma, Jeffreys' rule and Box & Tiao priors presented equal results, whereas Jeffreys and Reference priors showed the best results. The MDIP prior had a worse performance in all analyzed situations showing not to be indicated for Bayesian analysis of the PE distribution. A real data set is analyzed for illustrative purpose of the Bayesian approaches.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135798262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.15446/rce.v46n1.104427
Sunil Kumar, Monica Choudhary
In the present study, we explore the problem of estimation of finite population variance in simple random sampling (without replacement) by utilizing information of two auxiliary variables. A ratio cum exponential estimator has been proposed and its properties are studied to the first degree of approximation. To demonstrate the efficiency, members of the proposed estimator as well as other existing estimators are compared to the usual unbiased estimator. To study the performance, a simulation study is undertaken for both real and artificial population using R software. The suggested estimator is found to be more efficient than other existing estimators in terms of having minimum MSE.
{"title":"An Improved Estimator of finite Population Variance Using two Auxiliary Variable SRS","authors":"Sunil Kumar, Monica Choudhary","doi":"10.15446/rce.v46n1.104427","DOIUrl":"https://doi.org/10.15446/rce.v46n1.104427","url":null,"abstract":"In the present study, we explore the problem of estimation of finite population variance in simple random sampling (without replacement) by utilizing information of two auxiliary variables. A ratio cum exponential estimator has been proposed and its properties are studied to the first degree of approximation. To demonstrate the efficiency, members of the proposed estimator as well as other existing estimators are compared to the usual unbiased estimator. To study the performance, a simulation study is undertaken for both real and artificial population using R software. The suggested estimator is found to be more efficient than other existing estimators in terms of having minimum MSE.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135181562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.15446/rce.v46n1.102308
Shakti Prasad, Vinay Kumar Yadav
Some efficient product type exponential imputation methods are proposed in this article to tackle the problem of incomplete values in sampling theory. To investigate the effectiveness of proposed exponential methods, the behaviours of the considered estimators are compared in two scenarios: with and without nonresponse. The simulation studies show that the proposed resultant estimators outperform other existing estimators in this literature.
{"title":"Imputation of Missing Data Through Product Type Exponential Methods in Sampling Theory","authors":"Shakti Prasad, Vinay Kumar Yadav","doi":"10.15446/rce.v46n1.102308","DOIUrl":"https://doi.org/10.15446/rce.v46n1.102308","url":null,"abstract":"Some efficient product type exponential imputation methods are proposed in this article to tackle the problem of incomplete values in sampling theory. To investigate the effectiveness of proposed exponential methods, the behaviours of the considered estimators are compared in two scenarios: with and without nonresponse. The simulation studies show that the proposed resultant estimators outperform other existing estimators in this literature.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.15446/rce.v45n2.100837
N. Celik
The pairwise comparisons or post-hoc methods are used for determining the source of the difference of group means in one-way ANOVA. These methods are mostly depend on normality assumption. However, nonnormal distributions are more prevalent than normal distribution. Therefore, robust estimation methods become very important tools in statistical analysis. In this paper, we assume that the distribution of the error terms is Azzalini's skew $t$ and obtain the robust estimators in order to make post-hoc tests in one-way ANOVA. We use maximum likelihood (ML) methodology and compare this methodology with some of robust estimators like M estimator, Wave estimator, trimmed mean and modified maximum likelihood (MML) methodology with Monte Carlo simulation study. Simulation results show that the proposed methodology is more preferable. We also compare power values of the test statistics and conclude that the test statistics based on the ML estimators are more powerful than the test statistics based on other methods.
{"title":"Robust Post-Hoc Multiple Comparisons: Skew t Distributed Error Terms","authors":"N. Celik","doi":"10.15446/rce.v45n2.100837","DOIUrl":"https://doi.org/10.15446/rce.v45n2.100837","url":null,"abstract":"The pairwise comparisons or post-hoc methods are used for determining the source of the difference of group means in one-way ANOVA. These methods are mostly depend on normality assumption. However, nonnormal distributions are more prevalent than normal distribution. Therefore, robust estimation methods become very important tools in statistical analysis. In this paper, we assume that the distribution of the error terms is Azzalini's skew $t$ and obtain the robust estimators in order to make post-hoc tests in one-way ANOVA. We use maximum likelihood (ML) methodology and compare this methodology with some of robust estimators like M estimator, Wave estimator, trimmed mean and modified maximum likelihood (MML) methodology with Monte Carlo simulation study. Simulation results show that the proposed methodology is more preferable. We also compare power values of the test statistics and conclude that the test statistics based on the ML estimators are more powerful than the test statistics based on other methods.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41404442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.15446/rce.v45n2.98957
M. Ndiaye, S. Dabo‐Niang, P. Ngom
In this work, we consider a nonparametric prediction of a spatiofunctional process observed under a non-random sampling design. The proposed predictor is based on functional regression and depends on two kernels, one of which controls the spatial structure and the other measures the proximity between the functional observations. It can be considered, in particular, as a supervised classification method when the variable of interest belongs to a predefined discrete finite set. The mean square error and almost complete (or sure) convergence are obtained when the sample considered is a locally stationary α-mixture sequence. Numerical studies were performed to illustrate the behavior of the proposed predictor. The finite sample properties based on simulated data show that the proposed prediction method outperformsthe classical predictor which not taking into account the spatial structure.
{"title":"Nonparametric Prediction for Spatial Dependent Functional Data Under Fixed Sampling Design","authors":"M. Ndiaye, S. Dabo‐Niang, P. Ngom","doi":"10.15446/rce.v45n2.98957","DOIUrl":"https://doi.org/10.15446/rce.v45n2.98957","url":null,"abstract":"In this work, we consider a nonparametric prediction of a spatiofunctional process observed under a non-random sampling design. The proposed predictor is based on functional regression and depends on two kernels, one of which controls the spatial structure and the other measures the proximity between the functional observations. It can be considered, in particular, as a supervised classification method when the variable of interest belongs to a predefined discrete finite set. The mean square error and almost complete (or sure) convergence are obtained when the sample considered is a locally stationary α-mixture sequence. Numerical studies were performed to illustrate the behavior of the proposed predictor. The finite sample properties based on simulated data show that the proposed prediction method outperformsthe classical predictor which not taking into account the spatial structure.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44566642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.15446/rce.v45n2.92390
Edilberto Cepeda Cuervo, Jorge Armando Sicacha
In this paper we propose Bayesian methods to fit econometric regression models, including those where the variability is assumed to follow a regression structure. We formulate the main functions of the statistical R-package BSPADATA, developed according to the proposed methods to obtain posteriori parameter inferences. After that, we include results of simulated studies to illustrate the use of this package and the performance of the proposed methods. Finally, we provide studies to illustrate the applications of the models and compare our results with that obtained by maximum likelihood.
{"title":"Spatial Econometric Models: A Bayesian Approach","authors":"Edilberto Cepeda Cuervo, Jorge Armando Sicacha","doi":"10.15446/rce.v45n2.92390","DOIUrl":"https://doi.org/10.15446/rce.v45n2.92390","url":null,"abstract":"In this paper we propose Bayesian methods to fit econometric regression models, including those where the variability is assumed to follow a regression structure. We formulate the main functions of the statistical R-package BSPADATA, developed according to the proposed methods to obtain posteriori parameter inferences. After that, we include results of simulated studies to illustrate the use of this package and the performance of the proposed methods. Finally, we provide studies to illustrate the applications of the models and compare our results with that obtained by maximum likelihood.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48055583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.15446/rce.v45n2.98988
Juan Sosa, Jeimy-Paola Aristizabal
Considering the flexibility and applicability of Bayesian modeling, in this work we revise the main characteristics of two hierarchical models in a regression setting. We study the full probabilistic structure of the models along with the full conditional distribution for each model parameter. Under our hierarchical extensions, we allow the mean of the second stage of the model to have a linear dependency on a set of covariates. The Gibbs sampling algorithms used to obtain samples when fitting the models are fully described and derived. In addition, we consider a case study in which the plant size is characterized as a function of nitrogen soil concentration and a grouping factor (farm).
{"title":"Some Developments in Bayesian Hierarchical Linear Regression Modeling","authors":"Juan Sosa, Jeimy-Paola Aristizabal","doi":"10.15446/rce.v45n2.98988","DOIUrl":"https://doi.org/10.15446/rce.v45n2.98988","url":null,"abstract":"Considering the flexibility and applicability of Bayesian modeling, in this work we revise the main characteristics of two hierarchical models in a regression setting. We study the full probabilistic structure of the models along with the full conditional distribution for each model parameter. Under our hierarchical extensions, we allow the mean of the second stage of the model to have a linear dependency on a set of covariates. The Gibbs sampling algorithms used to obtain samples when fitting the models are fully described and derived. In addition, we consider a case study in which the plant size is characterized as a function of nitrogen soil concentration and a grouping factor (farm).","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48373538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.15446/rce.v45n2.96844
V. Zardasht
At the entropy measures and their generalization path, in the direction of statistics and information science, recently, Sunoj & Linu (2012) proposed the cumulative residual Renyi's entropy of order α and its dynamic version and studied its main properties. In this paper, we introduce an alternative measure of cumulative residual Renyi's entropy (CRRE) of order α which, unlike the mentioned one, is positive for all distributions and all values of α. We also consider its dynamic version and study their main properties in the context of reliability theory and stochastic orders. We give an estimator of the proposed CRRE and investigate its exact and asymptotic distribution. Numerous examples illustrating the theory are also given.
{"title":"On Cumulative Residual Renyi's Entropy","authors":"V. Zardasht","doi":"10.15446/rce.v45n2.96844","DOIUrl":"https://doi.org/10.15446/rce.v45n2.96844","url":null,"abstract":"At the entropy measures and their generalization path, in the direction of statistics and information science, recently, Sunoj & Linu (2012) proposed the cumulative residual Renyi's entropy of order α and its dynamic version and studied its main properties. In this paper, we introduce an alternative measure of cumulative residual Renyi's entropy (CRRE) of order α which, unlike the mentioned one, is positive for all distributions and all values of α. We also consider its dynamic version and study their main properties in the context of reliability theory and stochastic orders. We give an estimator of the proposed CRRE and investigate its exact and asymptotic distribution. Numerous examples illustrating the theory are also given.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48227388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.15446/rce.v45n2.98550
A. Bolívar-Cimé, Didier Cortez-Elizalde
The study of the structure of the covariance matrix when the dimension of the data is much greater than the sample size (high dimensional data) is a complicated problem, since we have many unknown parameters and few data. Several hypothesis tests for the covariance matrix, in the high dimensional context and in the classical case (where the dimension of the data is less than the sample size), can be found in the literature. It has been of interest the tests for the null hypothesis that the covariance matrix of Gaussian data is equal or proportional to the identity matrix, considering the classical case as well as the high dimensional context. Since it is important to have a wide comparison between these tests found in the literature, and for some of them it is difficult to have theoretical results about their powers, in this work we compare several tests by simulations, in terms of the size and power of the test. We also present some examples of application with real high dimensional data found in the literature.
{"title":"Behavior of Some Hypothesis Tests for the Covariance Matrix of High Dimensional Data","authors":"A. Bolívar-Cimé, Didier Cortez-Elizalde","doi":"10.15446/rce.v45n2.98550","DOIUrl":"https://doi.org/10.15446/rce.v45n2.98550","url":null,"abstract":"The study of the structure of the covariance matrix when the dimension of the data is much greater than the sample size (high dimensional data) is a complicated problem, since we have many unknown parameters and few data. Several hypothesis tests for the covariance matrix, in the high dimensional context and in the classical case (where the dimension of the data is less than the sample size), can be found in the literature. It has been of interest the tests for the null hypothesis that the covariance matrix of Gaussian data is equal or proportional to the identity matrix, considering the classical case as well as the high dimensional context. Since it is important to have a wide comparison between these tests found in the literature, and for some of them it is difficult to have theoretical results about their powers, in this work we compare several tests by simulations, in terms of the size and power of the test. We also present some examples of application with real high dimensional data found in the literature.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44787227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.15446/rce.v45n1.93548
Sher B. Chhetri, N. Mdziniso, Cory Ball
In this work, we propose a three-parameter generalized Lindley distribution using the cubic rank transmutation map approach by Granzotto, Louzada & Balakrishnan (2017). We derive expressions for several mathematical properties including moments and moment generating function, mean deviation, probability weighted moments, quantile function, reliability analysis, and order statistics. We conducted a simulation study to assess the performance of the maximum likelihood estimation procedure for estimating model parameters. The flexibility of the proposed model is illustrated by analyzing two real data sets.
{"title":"Extended Lindley Distribution with Applications","authors":"Sher B. Chhetri, N. Mdziniso, Cory Ball","doi":"10.15446/rce.v45n1.93548","DOIUrl":"https://doi.org/10.15446/rce.v45n1.93548","url":null,"abstract":"In this work, we propose a three-parameter generalized Lindley distribution using the cubic rank transmutation map approach by Granzotto, Louzada & Balakrishnan (2017). We derive expressions for several mathematical properties including moments and moment generating function, mean deviation, probability weighted moments, quantile function, reliability analysis, and order statistics. We conducted a simulation study to assess the performance of the maximum likelihood estimation procedure for estimating model parameters. The flexibility of the proposed model is illustrated by analyzing two real data sets.","PeriodicalId":54477,"journal":{"name":"Revista Colombiana De Estadistica","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48752694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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