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Bayesian Estimation in Shared Positive Stable Frailty Models

David D. Hanagal Asmita T. Kamble

https://doi.org/10.6339/JDS.201610_14(4).0003

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 14, Issue 4 (2016), pp. 615–640

Abstract

Abstract: The concept of frailty provides a suitable way to introduce random effects in the model to account for association and unobserved heterogeneity. In its simplest form, a frailty is an unobserved random factor that modifies multiplicatively the hazard function of an individual or a group or cluster of individuals. In this paper, we study positive stable distribution as frailty distribution and two different baseline distributions namely Pareto and linear failure rate distribution. We estimate parameters of proposed models by introducing Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique. In the present study a simulation is done to compare the true values of parameters with the estimated value. We try to fit the proposed models to a real life bivariate survival data set of McGrilchrist and Aisbett (1991) related to kidney infection. Also, we present a comparison study for the same data by using model selection criterion, and suggest a better model.

Estimation Methods for the New Weibull-Pareto Distribution: Simulation and Application

Ehab. M. Almetwally Hisham. M. Almongy

https://doi.org/10.6339/JDS.201907_17(3).0009

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 17, Issue 3 (2019), pp. 613–632

Abstract

In this paper, we introduce the alternative methods to estimation for the new weibull-pareto distribution parameters. We discussed of point estimation and interval estimation for parameters of the new weibull-pareto distribution. We have also discussed the method of Maximum Likelihood estimation, the method of Least Squares estimation, the method of Weighted Least Squares estimation and the method of Maximum Product Spacing estimation. In addition, we discussed the raw moment of random variable X and the reliability functions (survival and hazard functions). Further, we compared between the results of the methods that have been discussed using Monte Carlo Simulation method and application study.

Data-Syntactic Regression of Ill-Being

Gordon G. Bechtel

https://doi.org/10.6339/JDS.201410_12(4).0004

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 12, Issue 4 (2014), pp. 613–632

Abstract

Abstract: Panel data transcends cross-sectional data by tapping pooled inter- and intra-individual differences, along with between and within individual variation separately. In the present study these micro variations in ill-being are predicted by psychological indicators constructed from the British Household Panel Survey (BHPS). Panel regression effects are corrected for errors-in-variables, which attenuate slopes estimated by traditional panel regressions. These corrections reveal that unhappiness and life dissatisfaction are distinct variables that have different psychological causations.

To Do or Not To Do Business with a Country: A Robust Classification Approach

Kuntal Bhattacharyya Pratim Datta

https://doi.org/10.6339/JDS.201110_09(4).0008

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 9, Issue 4 (2011), pp. 607–623

Abstract

Abstract: In the face of global uncertainty and a growing reliance on third party indices to gain a snapshot of a country’s operations, accurate decision making makes or breaks relationships in global trade. Under this aegis, we question the validity of traditional logistic regression using the maximum likelihood estimator (MLE) in classifying countries for doing business. This paper proposes that a weighted version of the Bianco and Yohai (BY) estimator is a superlative and robust (outlier resistant) tool in the hands of practitioners to gauge the correct antecedents of a country’s internal environment and decide whether to do or not do business with that country. In addition, this robust process is effective in differentiating between “problem” countries and “safe” countries for doing business. An existing “R” program for the BY estimation technique by Croux and Haesbroeck has been modified to fit our cause.

Generalized Poisson-Poisson Mixture Model for Misreported Counts with an Application to Smoking Data

Mavis Pararai Felix Famoye

https://doi.org/10.6339/JDS.2010.08(4).608

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 8, Issue 4 (2010), pp. 607–617

Abstract

Abstract: The assumption that is usually made when modeling count data is that the response variable, which is the count, is correctly reported. Some counts might be over- or under-reported. We derive the Generalized PoissonPoisson mixture regression (GPPMR) model that can handle accurate, underreported and overreported counts. The parameters in the model will be estimated via the maximum likelihood method. We apply the GPPMR model to a real-life data set.

Tree-Based Missing Value Imputation Using Feature Selection

Heizel Rosado-Galindo Saylisse Dávila-Padilla

https://doi.org/10.6339/JDS.202010_18(4).0002

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 18, Issue 4 (2020), pp. 606–631

Abstract

Researchers and practitioners of many areas of knowledge frequently struggle with missing data. Missing data is a problem because almost all standard statistical methods assume that the information is complete. Consequently, missing value imputation offers a solution to this problem. The main contribution of this paper lies on the development of a random forest-based imputation method (TI-FS) that can handle any type of data, including high-dimensional data with nonlinear complex interactions. The premise behind the proposed scheme is that a variable can be imputed considering only those variables that are related to it using feature selection. This work compares the performance of the proposed scheme with other two imputation methods commonly used in literature: KNN and missForest. The results suggest that the proposed method can be useful in complex scenarios with categorical variables and a high volume of missing values, while reducing the amount of variables used and their corresponding preliminary imputations.

Properties and Data Modelling Applications of the Kumaraswamy Generalized Marshall-Olkin-G Family of Distributions

Subrata Chakraborty Laba Handique

https://doi.org/10.6339/JDS.201807_16(3).0009

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 16, Issue 3 (2018), pp. 605–620

Abstract

bstract: In this article we propose further extension of the generalized Marshall Olkin-G ( GMO - G ) family of distribution. The density and survival functions are expressed as infinite mixture of the GMO - G distribution. Asymptotes, Rényi entropy, order statistics, probability weighted moments, moment generating function, quantile function, median, random sample generation and parameter estimation are investigated. Selected distributions from the proposed family are compared with those from four sub models of the family as well as with some other recently proposed models by considering real life data fitting applications. In all cases the distributions from the proposed family out on top.

The Beta Lindley Distribution

S.M.T.K. MirMostafaee M. Mahdizadeh Saralees Nadarajah

https://doi.org/10.6339/JDS.201507_13(3).0010

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 13, Issue 3 (2015), pp. 603–626

Abstract

The Lindley distribution has been generalized by many authors in recent years. However, all of the known generalizations so far have restricted tail behaviors. Here, we introduce the most flexible generalization of the Lindley distribution with its tails controlled by two independent parameters. Various mathematical properties of the generalization are derived. Maximum likelihood estimators of its parameters are derived. Fisher’s information matrix and asymptotic confidence intervals for the parameters are given. Finally, a real data application shows that the proposed generalization performs better than all known ones

Analysis of Contagion in Emerging Markets

Juliana de P. Filleti Luiz K. Hotta Mauricio Zevallos

https://doi.org/10.6339/JDS.2008.06(4).419

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 6, Issue 4 (2008), pp. 601–626

Abstract

Abstract: The spread of crises from one country to another, named “con tagion”, has been one of the most debated issues in international finance in the last two decades. The presence of contagion can be detected by the increase in conditional correlation during the crisis period compared to the previous period. The paper presents a brief review of three of the most used techniques to estimate conditional correlation: exponential weighted mov ing average, multivariate GARCH models and factor analysis with stochastic volatility models. These methods are applied to analyze the contagion be tween the stock market of three major Latin American economies (Brazil, Mexico and Argentina) and two emerging markets (Malaysia and Russia). The data cover the period from 09/05/1995 to 12/30/2004, which includes several crises. In general, the three methods yielded similar results, but there is no general agreement. All the methods agreed that the contagion occurred mostly during the Asian crisis.

Application of Skew-normal in Classification of Satellite Image

Mohammad Reza Zadkarami Mahdi Rowhani

https://doi.org/10.6339/JDS.2010.08(4).624

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 8, Issue 4 (2010), pp. 597–606

Abstract

Abstract: The aim of this paper is to investigate the flexibility of the skewnormal distribution to classify the pixels of a remotely sensed satellite image. In the most of remote sensing packages, for example ENVI and ERDAS, it is assumed that populations are distributed as a multivariate normal. Then linear discriminant function (LDF) or quadratic discriminant function (QDF) is used to classify the pixels, when the covariance matrix of populations are assumed equal or unequal, respectively. However, the data was obtained from the satellite or airplane images suffer from non-normality. In this case, skew-normal discriminant function (SDF) is one of techniques to obtain more accurate image. In this study, we compare the SDF with LDF and QDF using simulation for different scenarios. The results show that ignoring the skewness of the data increases the misclassification probability and consequently we get wrong image. An application is provided to identify the effect of wrong assumptions on the image accuracy.

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Journal of data science

Online ISSN: 1683-8602
Print ISSN: 1680-743X

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