Statistical planning when the parameters in the model are of unequal importance

In this paper, we consider statistical planning of experiments when the parameters in the linear model assumed are divided into disjoint sets; the parameters in one set are more influential than the parameters in the other set and require more precise estimation. Me characterize the plans (or, designs) and discuss some symmetric designs which are easier to find.     

Two sample nonparametric tests based on subsamples

The paper introduces a general class of nonparametric tests for the two-sample location problem based on subsamples. Includ- ed in this class is the Mann-Whitney (or the Wilcoxon rank sum) test. General formulas for the Pitman efficacy for different methods of subsampling are derived. A small sample power simu- lation compares the performance of members of this class     

Construction on non-adaptive hypergeometric group testing desings for identifying at most two defectives

In the literature a systematic method of obtaining a group testing design is not available at present. Weideman and Raghavarao (1987a, b) gave methods for the construction of non - adaptive hypergeometric group testing designs for identifying at most two defectives by using a dual method. In the present investigation we have developed a method of construction of group testing designs from (i) Hypercubic Designs for t ≡ 3 (mod 6) and (ii) Balanced Incomplete Block Designs for t ≡ 1 (mod 6) and t ≡ 3 (mod 6). These constructions are accomplished by the use of dual designs. The designs so constructed satisfy specified properties and attained an optimal bound as discussed by Weidman and Raghavarao (1987a,b). Here it is also shown that the condition for pairwise disjoint sets of BIBD for t ≡ 1 (mod 6) given by Weideman and Raghavarao (1987b) is not true for all such designs.     

Normality testing for a long-memory sequence using the empirical moment generating function

Moment generating functions and more generally, integral transforms for goodness-of-fit tests have been in use in the last several decades. Given a set of observations, the empirical transforms are easy to compute, being simply a sample mean, and due to uniqueness properties, these functions can be used for goodness-of-fit tests. This paper focuses on time series observations from a stationary process for which the moment generating function exists and the correlations have long-memory. For long-memory processes, the infinite sum of the correlations diverges and the realizations tend to have spurious trend like patterns where there may be none. Our aim is to use the empirical moment generating function to test the null hypothesis that the marginal distribution is Gaussian. We provide a simple proof of a central limit theorem using ideas from Gaussian subordination models (Taqqu, 1975) and derive critical regions for a graphical test of normality, namely the T₃-plot ( Ghosh, 1996). Some simulated and real data examples are used for illustration.     

Secure Bayesian model averaging for horizontally partitioned data

When multiple data owners possess records on different subjects with the same set of attributes—known as horizontally partitioned data—the data owners can improve analyses by concatenating their databases. However, concatenation of data may be infeasible because of confidentiality concerns. In such settings, the data owners can use secure computation techniques to obtain the results of certain analyses on the integrated database without sharing individual records. We present secure computation protocols for Bayesian model averaging and model selection for both linear regression and probit regression. Using simulations based on genuine data, we illustrate the approach for probit regression, and show that it can provide reasonable model selection outputs.     

A new class of generalized logistic distribution

The logistic distribution and the S-shaped pattern of its cumulative distribution and quantile functions have been extensively used in many different spheres affecting human life. By far, the most well-known application of logistic distribution is in the logistic regression that is used for modeling categorical response variables. The exponentiated-exponential logistic distribution, a generalization of the logistic distribution, is obtained using the technique proposed by Alzaatreh et al. (2013 Alzaatreh, A., C. Lee, and F. Famoye. 2013. A new method for generating families of continuous distribution. Metron. 71:63–79.[Crossref] , [Google Scholar]) of mixing two distributions, hereafter called the EEL distribution. This distribution subsumes various types of logistic distribution. The structural analysis of the distribution in this paper includes limiting behavior, quantiles, moments, mode, skewness, kurtosis, order statistics, the large sample distributions of the sample maximum and the sample minimum, and the distribution of the sample median. For illustrative purposes, a real-life data set is considered as an application of the EEL distribution.     

Strategic planning—A contingency approach—Part 1. The strategic analysis

This article emphasizes a certain approach to business strategy formation exercise, i.e. the adaptive mode. As the 1970s emerge into the uncertain 1980s, and as sophisticated extrapolation is becoming increasingly valueless, there is an even greater case for business planning with the required degree of responsiveness built into it. The authors support this case from literature survey and an actual case study. This is the first of two articles on this subject.     

On some new search designs for 2m factorial experiments

In this paper, we obtain search designs with reasonably small number of treatments which permit the estimation of the general mean and main effects and search of one more unknown possible nonzero effect among two and three factor interactions in 2^m factorial experiments, 3 ? m ? 8.     

A flexible class of models for data arising from a ‘thorough QT/QTc study’

The standard methods for analyzing data arising from a ‘thorough QT/QTc study’ are based on multivariate normal models with common variance structure for both drug and placebo. Such modeling assumptions may be violated and when the sample sizes are small, the statistical inference can be sensitive to such stringent assumptions. This article proposes a flexible class of parametric models to address the above‐mentioned limitations of the currently used models. A Bayesian methodology is used for data analysis and models are compared using the deviance information criteria. Superior performance of the proposed models over the current models is illustrated through a real dataset obtained from a GlaxoSmithKline (GSK) conducted ‘thorough QT/QTc study’. Copyright © 2010 John Wiley & Sons, Ltd.