Statistical inference based on generalized Lindley record values

This paper addresses the problems of frequentist and Bayesian estimation for the unknown parameters of generalized Lindley distribution based on lower record values. We first derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariance between two lower record values. We next obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Furthermore, we obtain Bayes estimators under the assumption of gamma priors on both the shape and the scale parameters of the generalized Lindley distribution, and associated the highest posterior density interval estimates. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential (LINEX)) loss functions. Finally, we compute Bayesian predictive estimates and predictive interval estimates for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.     

A note on positive dependence im multivariate distributioms

I counterexample is presented to the result by Alam and Malleolus asserting that if Y is stochastically increasing in a random vector X, then Y is stochastically increasing in a subvector of X. Their result concerning m*-positive dependence, whose proof relies on the erroneous result, is still true.     

Mating strategies of young women: role of physical attractiveness

The female physical attractiveness stereotype has been reported to contain both desirable (sociable, poised, interesting) and undesirable (snobbish, likely to request divorce and have extra-marital affairs) personal qualities. To investigate whether such an attractiveness stereotype is cross-cultural, I asked men and women from Azore Island, Guinea-Bissau, Indonesia, and the U.S. to judge the attractiveness of female figures differing in body weight and waist-to-hip ratio (WHR) and to rank these figures according to perceived personal attributes. There was a strong cross-cultural consensus for attractiveness; figures with low WHR were judged to be more attractive than figures with high WHR within each weight category. Participants also judged attractive figures as less faithful than less-attractive figures. To explore the basis of a possible 'darker side ' of the attractiveness stereotype, behavior tactics of young U.S. women were examined. Compared to women with high WHRs, low-WHR women reported engaging in more flirting to make dates jealous, suggesting some truth to the attractiveness stereotype. Taken together, these findings suggest that female attractiveness influences the type of mating strategies employed by women.     

International Evidence on the Determinants of Organizational Ethical Vulnerability

This paper proposes a model to explain what makes organizations ethically vulnerable. Drawing upon legitimacy, institutional, agency and individual moral reasoning theories we consider three sets of explanatory factors and examine their association with organizational ethical vulnerability. The three sets comprise external institutional context, internal corporate governance mechanisms and organizational ethical infrastructure. We combine these three sets of factors and develop an analytical framework for classifying ethical issues and propose a new model of organizational ethical vulnerability. We test our model on a sample of 253 firms that were involved in ethical misconduct and compare them with a matched sample of the same number of firms from 28 different countries. The results suggest that weak regulatory environment and internal corporate governance, combined with profitability warnings or losses in the preceding year, increase organizational ethical vulnerability. We find counterintuitive evidence suggesting that firms’ involvement in bribery and corruption prevention training programmes is positively associated with the likelihood of ethical vulnerability. By synthesizing insights about individual and corporate behaviour from multiple theories, this study extends existing analytical literature on business ethics. Our findings have implications for firms’ external regulatory settings, corporate governance mechanisms and organizational ethical infrastructure.     

The innovation dynamics and technology cycles in the computer industry

This paper presents certain novel composite measures of technology. There are several important uses of the proposed measures in R & D management: to gauge the rate of innovation within any given field, to determine the relative contribution of chosen variables to technical progress, to isolate critical factors in the innovative activity, and to predict whether a technology is ahead or behind its time. This is illustrated here by means of a case study of technical progress in the computer industry. The theoretical framework of the case study is based on the earlier works of the author pointing to the existence of what may be called technology cycles that transcend both product life cycle and organization product cycle and have the same significance to innovative activity as have business cycles to economic activity. The results of our investigation reveal a ‘butterfly pattern’ of interaction between product and process innovations: The loss in the relevance of process innovations is almost exactly compensated by the gain in the relevance of product innovations to technical progress over the course of time. The results also indicate that although the pace of technical progress in the computer industry has been exceptionally rapid in the past, it shows signs of slowing down in the future. The policy implications of these findings are discussed.     

Exponentiated Chen distribution: Properties and estimation

This article addresses various properties and estimation methods for the Exponentiated Chen distribution. Although, our main focus is on estimation from frequentist point of view, yet, some statistical and reliability characteristics for the model are derived. We briefly describe different estimation procedures, namely, the method of maximum likelihood estimation, percentile estimation, least square and weighted least-square estimation, maximum product of spacings estimation, Cramér-von-Mises estimation, Anderson–Darling, and right-tail Anderson–Darling estimation. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation for both small and large samples. Finally, the potentiality of the model is analyzed by means of three real datasets.     

Extended exponential distribution based on order statistics

The extended exponential distribution due to Nadarajah and Haghighi (2011 Nadarajah, S., Haghighi, F. (2011). An extension of the exponential distribution. Statistics 45:543–558.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) is an alternative to and always provides better fits than the gamma, Weibull, and the exponentiated exponential distributions whenever the data contain zero values. We establish recurrence relations for the single and product moments of order statistics from the extended exponential distribution. These recurrence relations enable computation of the means, variances, and covariances of all order statistics for all sample sizes in a simple and efficient manner. By using these relations, we tabulate the means, variances, and covariances of order statistics and derive best linear unbiased estimates of the extended exponential distribution. Finally, a data application is provided.     

Estimation and prediction for a Burr type-III distribution with progressive censoring

We consider estimation of unknown parameters and reliability characteristics of a Burr type-III distribution under progressive censoring. Predictive estimates for censored observations and the associated prediction intervals are also obtained. We derive maximum-likelihood estimators of unknown quantities using the EM algorithm and then also obtain the observed Fisher information matrix. We provide various Bayes estimators for unknown parameters under the squared error loss function. Highest posterior density and asymptotic intervals are also constructed. We evaluate performance of proposed methods using simulations. Finally, an illustrative example is presented in support of the methods discussed.     

Inverse Lindley power series distributions: a new compounding family and regression model with censored data

This paper introduces a new class of distributions by compounding the inverse Lindley distribution and power series distributions which is called compound inverse Lindley power series (CILPS) distributions. An important feature of this distribution is that the lifetime of the component associated with a particular risk is not observable, rather only the minimum lifetime value among all risks is observable. Further, these distributions exhibit an unimodal failure rate. Various properties of the distribution are derived. Besides, two special models of the new family are investigated. The model parameters of the two sub-models of the new family are obtained by the methods of maximum likelihood, least square, weighted least square and maximum product of spacing and compared them using the Monte Carlo simulation study. Besides, the log compound inverse Lindley regression model for censored data is proposed. Three real data sets are analyzed to illustrate the flexibility and importance of the proposed models.     

Estimation and prediction for Chen distribution with bathtub shape under progressive censoring

We consider estimation of the unknown parameters of Chen distribution [Chen Z. A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function. Statist Probab Lett. 2000;49:155–161] with bathtub shape using progressive-censored samples. We obtain maximum likelihood estimates by making use of an expectation–maximization algorithm. Different Bayes estimates are derived under squared error and balanced squared error loss functions. It is observed that the associated posterior distribution appears in an intractable form. So we have used an approximation method to compute these estimates. A Metropolis–Hasting algorithm is also proposed and some more approximate Bayes estimates are obtained. Asymptotic confidence interval is constructed using observed Fisher information matrix. Bootstrap intervals are proposed as well. Sample generated from MH algorithm are further used in the construction of HPD intervals. Finally, we have obtained prediction intervals and estimates for future observations in one- and two-sample situations. A numerical study is conducted to compare the performance of proposed methods using simulations. Finally, we analyse real data sets for illustration purposes.