Journal of Data Science

Abstract: Longitudinal studies represent one of the principal research strategies employed in medical and social research. These studies are the most appropriate for studying individual change over time. The prematurely withdrawal of some subjects from the study (dropout) is termed nonrandom when the probability of missingness depends on the missing value. Nonrandom dropout is common phenomenon associated with longitudinal data and it complicates statistical inference. Linear mixed effects model is used to fit longitudinal data in the presence of nonrandom dropout. The stochastic EM algorithm is developed to obtain the model parameter estimates. Also, parameter estimates of the dropout model have been obtained. Standard errors of estimates have been calculated using the developed Monte Carlo method. All these methods are applied to two data sets.

Abstract: A new rank-based test statistics are proposed for the problem of a possible change in the distribution of independent observations. We extend the two-sample test statistic of Damico (2004) to the change point setup. The finite sample critical values of the proposed tests is estimated. We also conduct a Monte Carlo simulation to compare the powers of the new tests with their competitors. Using the Nile data of Cobb (1978), we demonstrate the applicability of the new tests.

Longitudinal data analysis had been widely developed in the past three decades. Longitudinal data are common in many fields such as public health, medicine, biological and social sciences. Longitudinal data have special nature as the individual may be observed during a long period of time. Hence, missing values are common in longitudinal data. The presence of missing values leads to biased results and complicates the analysis. The missing values have two patterns: intermittent and dropout. The missing data mechanisms are missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). The appropriate analysis relies heavily on the assumed mechanism and pattern. The parametric fractional imputation is developed to handle longitudinal data with intermittent missing pattern. The maximum likelihood estimates are obtained and the Jackkife method is used to obtain the standard errors of the parameters estimates. Finally a simulation study is conducted to validate the proposed approach. Also, the proposed approach is applied to a real data.

Abstract: Missing values are not uncommon in longitudinal data studies. Missingness could be due to withdrawal from the study (dropout) or intermittent. The missing data mechanism is termed non-ignorable if the probability of missingness depends on the unobserved (missing) observations. This paper presents a model for continuous longitudinal data with non-ignorable non-monotone missing values. Two separate models, for the response and missingness, are assumed. The response is modeled as multivariate nor mal whereas the binomial model for missingness process. Parameters in the adopted model are estimated using the stochastic EM algorithm. The proposed model (approach) is then applied to an example from the International Breast Cancer Study Group.

In this article a new Bayesian regression model, called the Bayesian semi-parametric logistic regression model, is introduced. This model generalizes the semi-parametric logistic regression model (SLoRM) and improves its estimation process. The paper considers Bayesian and non-Bayesian estimation and inference for the parametric and semi-parametric logistic regression model with application to credit scoring data under the square error loss function. The paper introduces a new algorithm for estimating the SLoRM parameters using Bayesian theorem in more detail. Finally, the parametric logistic regression model (PLoRM), the SLoRM and the Bayesian SLoRM are used and compared using a real data set.