For the analysis of square contingency tables with the same row and column ordinal classifications, this article pro- poses a new model, which may be appropriate for a square ordinal table if it is reasonable to assume an underlying bivariate t-distribution with equal marginal variances having any degrees of freedom. The proposed model indicates that the difference between the symmetric two probabilities raised to the power is proportional to the distance from the main diagonal of the table. The model is an extension of the symmetry model. An example is given. The simulation studies based on bivariate t-distribution are given.

Sarjinder Singh, Editor-in-Chief of the journal Model Assisted Statistics and Applications (MASA) organized a booth STAT-HAWKERS at the Joint Statistical Meeting (JSM) of the American Statistical Association, during July 28 August 2, 2012, in San Diego, CA. The First International Meeting of the MASA members of the Editorial Board, reviewers, and guests of the journal, in July 31, at the JSM is shown in Picture 1. The meeting was chaired by Stan Lipovetsky, Co-Editor-in-Chief of MASA. The topics of the discussion included how to promote the journal and to increase its citation rate. Professors Igor Mandel and Vladimir Ladyzhets suggested various ways for solving such problems. Reviewers and would be reviewers Jingjing Doris Gao, Hugh Gong, Fang Liu, and Shawn Odumade shared their thoughts on the subject Professor Wenyaw Chan has been organizing a special issue on “Recent developments in statistical methods for medical research”.

This paper presents an analysis of copula models in comparison to standard statistical distributions in the context of international securities markets. Due to the fact that copula models require independent and identically distributed (i.i.d.) rando

In present work, best linear unbiased estimators have been proposed to estimate the population variance on current occasion in two-occasion successive (rotation) sampling. Optimum replacement policies of the proposed estimators are discussed. Results are supported with the suitable empirical studie s.

The analytic hierarchy process (AHP) is a structured technique for dealing with complex decisions. In this paper, we propose a new AHP technique using Geographic Information System (GIS) termed as “Objective Spatial Analytic Hierarchy Process (OSAHP)

Horticulture sector is an important sub-sector of agriculture with its contribution of about 20% in the Indian economy of agriculture and allied sectors. Fruits and vegetables account for nearly 90% of total horticulture production in the country. Although, India has emerged as a leading producer o f these crops in the world, we do not have official estimates on large number of fruits and vegetables. In this paper, we propose an alternative sampling methodology for estimation of area and production of different horticultural crops. The study has revealed very encouraging results and demonstrated the feasibility of estimating the production of fruits and vegetables with much smaller sample size.

In this paper we assume that the patient's health evolves according to a Markov chain with finite state space. To each state of illness is assigned a score through a state function. We consider the possibility to have different state functions at som

Comparisons of estimates between the Bayes and frequentist methods are fascinating and challenging theme of interest in statistics with significant impact on professionals. For Bayes estimators, the performance depends on the form of the prior distribution and the assumed loss function. This paper illu strates the problem of estimation of the one-parameter generalized geometric series distribution, using conjugate and improper prior distributions under symmetric and asymmetric loss functions. Performance of the Bayes estimates with respect to different priors, loss functions, and maximum likelihood estimates are illustrated for a data set and through a simulation study.

Decision makers in organizations frequently need to determine relative importance of multiple predictors of an important Dependent Variable. Using Multiple Regression for this purpose is often challenging when predictors are intercorrela ted. In this paper we present the results of two Monte-Carlo studies comparing the effectiveness of two methods for determining relative importance of predictors under conditions of multicollinearity: Johnson's Relative Weights (JRW) and Breiman's Rand om Forests (RF). The following factors were systematically varied: number of predictors, correlations among predictors in population, regression model R 2 in population, number of observations per predictor, relia bility of measurement, and standard deviation of regression coefficients across predictors in population. T o serve as a benchmark measure of the relative importance of predictors, General Dominance Weights (GDW) method was used (also known as Shapley Value of the predictors in a regression), which defines predictor importance as an average increase in R 2 associated with that predictor across all possible regress ion submodels. Sample-based predictor importances determined by JRW and RF were compared to GDW importances in population. The implications of the results for practitioners are discusse d.

Coefficients of ordinary least squares (OLS) multiple regression present the best linear combination of the independent variables for fitting a dependent variable. However, the OLS model had never been designed to produce meaningful individual predic