Strategic planning—A contingency approach—Part 2. The plan

This is the second article on the subject of the adaptive approach to business strategy formation exercise and illustrates the approach with a case study. Part 1 appeared in the August 1983 issue of the Journal.     

Accelerated Rates Regression Models for Recurrent Failure Time Data

In this article, we formulate a semiparametric model for counting processes in which the effect of covariates is to transform the time scale for a baseline rate function. We assume an arbitrary dependence structure for the counting process and propose a class of estimating equations for the regression parameters. Asymptotic results for these estimators are derived. In addition, goodness of fit methods for assessing the adequacy of the accelerated rates model are proposed. The finite-sample behavior of the proposed methods is examined in simulation studies, and data from a chronic granulomatous disease study are used to illustrate the methodology.     

Nonparametric estimation of the conditional mean residual life function with censored data

The conditional mean residual life (MRL) function is the expected remaining lifetime of a system given survival past a particular time point and the values of a set of predictor variables. This function is a valuable tool in reliability and actuarial studies when the right tail of the distribution is of interest, and can be more informative than the survivor function. In this paper, we identify theoretical limitations of some semi-parametric conditional MRL models, and propose two nonparametric methods of estimating the conditional MRL function. Asymptotic properties such as consistency and normality of our proposed estimators are established. We investigate via simulation study the empirical properties of the proposed estimators, including bootstrap pointwise confidence intervals. Using Monte Carlo simulations we compare the proposed nonparametric estimators to two popular semi-parametric methods of analysis, for varying types of data. The proposed estimators are demonstrated on the Veteran’s Administration lung cancer trial.     

A Dynamic Model of Product Quality and Pricing Decisions on Sales Response

This paper presents a dynamic model that examines the influence of price and customer perception of product quality on the sales rate of a firm producing durable goods. The sales rate of the firm is modeled as a function of its price, average product life, perceived quality, and market potential. Specifically, the model considers: (1) the process by which perceived quality is determined by including the effects of the average life of a product and quality weighted units in the market; (2) the time delay in the influence of actual quality on perceived quality; (3) the process by which demand (i.e., potential sales) is converted to realized sales due to the effects of price and perceived quality; and (4) the saturation effect and associated non-linearity in the demand function. The model presented is a reformulation and extension of the model originally proposed by Bass [2]. The validity of the model is tested using historical data on For Mustang.     

Bayesian variable selection using an adaptive powered correlation prior

The problem of selecting the correct subset of predictors within a linear model has received much attention in recent literature. Within the Bayesian framework, a popular choice of prior has been Zellner's g$g$-prior which is based on the inverse of empirical covariance matrix of the predictors. An extension of the Zellner's prior is proposed in this article which allow for a power parameter on the empirical covariance of the predictors. The power parameter helps control the degree to which correlated predictors are smoothed towards or away from one another. In addition, the empirical covariance of the predictors is used to obtain suitable priors over model space. In this manner, the power parameter also helps to determine whether models containing highly collinear predictors are preferred or avoided. The proposed power parameter can be chosen via an empirical Bayes method which leads to a data adaptive choice of prior. Simulation studies and a real data example are presented to show how the power parameter is well determined from the degree of cross-correlation within predictors. The proposed modification compares favorably to the standard use of Zellner's prior and an intrinsic prior in these examples.     

An exploration of gratitude themes and suggestions for future interventions

For many years, the concept of gratitude was not considered worthy of investigation by psychologists since research goals were strongly dominated by the deficit-based approach and gratitude concepts were believed to be derived from common sense, and thus not warranting scientific query. This study, in attempting to understand gratitude themes, explores various events that elicit gratitude among engineering students in India. Thirty-two final year engineering students were introduced to gratitude research, administered gratitude exercises, and asked to provide feedback about the exercises. Nested questions, in the form of three-blessings and benefit finding exercises, were used to identify experiences of students leading to gratitude. Content analysis was applied to identify underlying themes and report them systematically. Four major themes and five minor themes were uncovered. Among these, positive emotions was the most prevalent theme and finance was the least encountered theme. Strategies to improve intervention studies, including restricting the area of investigation to one domain and making exercises interesting, have been suggested. Implications of the research within the socio-cultural context have been discussed with special reference to the complexities in separating the sense of duty and gratitude. Further research will provide us with more insights into the topic, specifically from an Indian socio-cultural perspective.     

On the hidden truncated bivariate Pareto (IV) model and associated inferential issues

Income and wealth data are typically modelled by some variant of the classical Pareto distribution. Often, in practice, the observed data are truncated with respect to some unobserved covariate. In this paper, a hidden truncation formulation of this scenario is proposed and analysed. For this purpose, a bivariate Pareto (IV) distribution is assumed for the variable of interest and the unobserved covariate. Some important distributional properties of the resulting model as well as associated inferential methods are studied. An example is used finally to illustrate the results developed here. In this case, it is noted that hidden truncation on the left does not result in any new model, but the hidden truncation on the right does. The properties and fit of such a model pose a challenging problem and that is what is focused here in this work.     

Divergence based robust estimation of the tail index through an exponential regression model

The extreme value theory is very popular in applied sciences including finance, economics, hydrology and many other disciplines. In univariate extreme value theory, we model the data by a suitable distribution from the general max-domain of attraction characterized by its tail index; there are three broad classes of tails—the Pareto type, the Weibull type and the Gumbel type. The simplest and most common estimator of the tail index is the Hill estimator that works only for Pareto type tails and has a high bias; it is also highly non-robust in presence of outliers with respect to the assumed model. There have been some recent attempts to produce asymptotically unbiased or robust alternative to the Hill estimator; however all the robust alternatives work for any one type of tail. This paper proposes a new general estimator of the tail index that is both robust and has smaller bias under all the three tail types compared to the existing robust estimators. This essentially produces a robust generalization of the estimator proposed by Matthys and Beirlant (Stat Sin 13:853–880, 2003) under the same model approximation through a suitable exponential regression framework using the density power divergence. The robustness properties of the estimator are derived in the paper along with an extensive simulation study. A method for bias correction is also proposed with application to some real data examples.     

Housing Opportunities for People Everywhere (HOPE VI): Who gets back in?

Housing Opportunities for People Everywhere, or commonly known as HOPE VI, under the auspices of the U.S. Department of Housing and Urban Development (HUD), has a mission to rebuild severely distressed public housing. In general the goals of HOPE VI projects are to improve severely distressed public housing and to improve the lives of its residents. While evidences around the country either lend support to these goals, or find anomalies, we find severe contradictions in a specific mid-south HOPE VI project. The question investigated is whether one particular HOPE VI project was efficacious in accomplishing these goals or was it deleterious to the residents and to what extent. The study began with straightforward comparisons of the original residents and the residents selected for resettlement in a HOPE VI reconstruction project. The investigation continued with a regression analysis of the factors that were significant in determining if a head of household was accepted for resettlement. The results of this analysis clearly demonstrate that there is a bias in the selection of those individuals accepted for resettlement.     

Statistical learning theory for fitting multimodal distribution to rainfall data: an application

The promising methodology of the “Statistical Learning Theory” for the estimation of multimodal distribution is thoroughly studied. The “tail” is estimated through Hill's, UH and moment methods. The threshold value is determined by nonparametric bootstrap and the minimum mean square error criterion. Further, the “body” is estimated by the nonparametric structural risk minimization method of the empirical distribution function under the regression set-up. As an illustration, rainfall data for the meteorological subdivision of Orissa, India during the period 1871–2006 are used. It is shown that Hill's method has performed the best for tail density. Finally, the combined estimated “body” and “tail” of the multimodal distribution is shown to capture the multimodality present in the data.