Statistical Modelling of Brain Morphological Measures Within Family Pedigrees

Large, family-based imaging studies can provide a better understanding of the interactions of environmental and genetic influences on brain structure and function. The interpretation of imaging data from large family studies, however, has been hindered by the paucity of well-developed statistical tools for that permit the analysis of complex imaging data together with behavioral and clinical data. In this paper, we propose to use two methods for these analyses. First, a variance components model along with score statistics is used to test linear hypotheses of unknown parameters, such as the associations of brain measures (e.g., cortical and subcortical surfaces) with their potential genetic determinants. Second, we develop a test procedure based on a resampling method to assess simultaneously the statistical significance of linear hypotheses across the entire brain. The value of these methods lies in their computational simplicity and in their applicability to a wide range of imaging data. Simulation studies show that our test procedure can accurately control the family-wise error rate. We apply our methods to the detection of statistical significance of gender-by-age interactions and of the effects of genetic variation on the thickness of the cerebral cortex in a family study of major depressive disorder.     

Multi-character survery using randomized response technique

In the present paper, we have consisdered the situation of multi–character survey where the study variables, beside being poorly correlated with the selection probabilities are also sensitive in nature. Randomized Response technique (RRT) proposed by Chaudhuri and Adhikary (1990) is used to elicit the information on the sensitive character. The empirical study carried out shows the relative efficiency of the transformation suggested by Basnel and Singh (1985) over the transformations suggested by Rao (1966) and Amahia et al.(1989) under a super population model.     

Partially balanced incomplete block designs with two associate classes

We provide constructions of cyclic 2-class PBIBD's using cyclotomy in finite fields. Our results give theoretical explanations of the two sporadic examples given by Agrawal (1987).     

Bayesian optimization analysis with ML-II ε-contaminated prior

In this paper we derive the predictive density function of a future observation when prior distribution for unknown mean of a normal population is a Type-II maximum likelihood ε-contaminated prior. The derived predictive distribution is applied to the problem of optimization of a regression nature in the decisive prediction framework.     

Estimating hybrid frequency moments of data streams

We consider the problem of estimating hybrid frequency moments of two dimensional data streams. In this model, data is viewed to be organized in a matrix form (A _i,j )_1≤i,j,≤n . The entries A _i,j are updated coordinate-wise, in arbitrary order and possibly multiple times. The updates include both increments and decrements to the current value of A _i,j . The hybrid frequency moment F _p,q (A) is defined as \(\sum_{j=1}^{n}(\sum_{i=1}^{n}{A_{i,j}}^{p})^{q}\) and is a generalization of the frequency moment of one-dimensional data streams.We present the first \(\tilde{O}(1)\) space algorithm for the problem of estimating F _p,q for p∈[0,2] and q∈[0,1] to within an approximation factor of 1±ε. The \(\tilde{O}\) notation hides poly-logarithmic factors in the size of the stream m, the matrix size n and polynomial factors of ε ^?1. We also present the first \(\tilde{O}(n^{1-1/q})\) space algorithm for estimating F _p,q for p∈[0,2] and q∈(1,2].     

Predicting effective customer lifetime: an application of survival analysis for telecommunication industry

Abstract

Innovation helps brands in making customer’s lives better and more meaningful. The purpose of brand management is to create an impact that makes differences to its customers. This study describes certain demographic factors that usually impact the continuing probability of customers and thereby can help management in identifying the effective lifetime of customers towards a particular offering. For this purpose the technique of Survival Analysis has been used and the data has been collected from customers of telecommunication industries. Results depicted that the attitudinal aspect of customers for continuing a particular product is significantly impacted by the factors under consideration.     

Older and Wiser: How CEOs’ Time Perspective Influences Long‐Term Investments in Environmentally Responsible Technologies

Most theories of corporate governance argue that chief executive officers (CEOs) take less risk as they near the end of their career, and therefore are less likely to make major investments. This prediction is based on decisions related to firm‐specific benefits; however, it may not be generalizable to decisions that involve broad societal goals. In terms of societal investments, CEOs with a longer time perspective may be more likely, rather than less likely, to invest. In this paper, we argue that a CEO's future time perspective is fostered by shorter career horizons, longer tenures, higher organizational ownership and less short‐term compensation. We test these hypotheses on 150 observations from the US investor‐owned electric power generation sector over a three‐year unbalanced sample (64.3% of the population). We applied random‐effects generalized least squares (GLS) estimations to test our hypotheses, and found support for three out of four hypothesized relationships.     

Bayes Estimation of Intraclass Correlation Coefficients Under Unequal Family Sizes

Bayesian inference for the intraclass correlation ρ is considered under unequal family sizes. We obtain the posterior distribution of ρ and then compare the performance of the Bayes estimator (posterior mean of ρ) with that of Srivastava's (1984) estimator through simulation. Simulation study shows that the Bayes estimator performs better than the Srivastava's estimator in terms of lower mean square error. We also obtain large sample posteriors of ρ based on the asymptotic posterior distribution and based on the Laplace approximation.     

Bayes Prediction for a Stratified Regression Superpopulation Model Using Balanced Loss Function

We consider the stratified regression superpopulation model and obtain Bayes predictor of the finite population mean under Zellner's two-criterion balanced loss function (BLF). BLF predictor simplifies to a linear combination of the sample and predictive means. Furthermore, it reduces to some of the well-known classical and Bayes predictors. Relative losses and relative savings loss are obtained to investigate loss robustness of the BLF predictor. It is found to perform better than the usual sample mean as well as the predictive mean in the minimal Bayes predictive expected loss sense.     

Testing Homogeneity of Gini Indices Against Simple-Ordered Alternative

Gini index is one of the most popular indices employed for measuring inequality in income and wealth. In this article, we propose a multi-sample test for testing Gini indices against simple-ordered alternatives. The exact critical points through simulation were computed for the proposed test in case of two-parameter exponential distribution; however, the proposed test can be applied even in case of other distributions like Pareto, Weibull, etc. Construction of simultaneous one-sided confidence intervals (SOCIs) was proposed along with an illustration. Power of the test is computed and some power comparisons are also made.