A bayesian approach to detect informative observations in an experiment

In this article Lindley's (1956) measure of average information is used to measure the loss of information due to the unavailability of a set of observations in an experiment. This measure of loss of information may be used to detect a set of most informative observations in a given design.     

Moments of order statistics of Topp–Leone distribution

We derive explicit algebraic expressions for both of the single and product moments of order statistics from Topp–Leone distribution. We also give an identity about single moments of order statistics. These expressions will be useful for computational purposes.     

Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm

In statistical analysis, one of the most important subjects is to select relevant exploratory variables that perfectly explain the dependent variable. Variable selection methods are usually performed within regression analysis. Variable selection is implemented so as to minimize the information criteria (IC) in regression models. Information criteria directly affect the power of prediction and the estimation of selected models. There are numerous information criteria in literature such as Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). These criteria are modified for to improve the performance of the selected models. BIC is extended with alternative modifications towards the usage of prior and information matrix. Information matrix-based BIC (IBIC) and scaled unit information prior BIC (SPBIC) are efficient criteria for this modification. In this article, we proposed a combination to perform variable selection via differential evolution (DE) algorithm for minimizing IBIC and SPBIC in linear regression analysis. We concluded that these alternative criteria are very useful for variable selection. We also illustrated the efficiency of this combination with various simulation and application studies.     

Bayesian quantile regression for ordinal longitudinal data

Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.     

Energy performance evaluation of OECD countries using Bayesian stochastic frontier analysis and Bayesian network classifiers

More recently a large amount of interest has been devoted to the use of Bayesian methods for deriving parameter estimates of the stochastic frontier analysis. Bayesian stochastic frontier analysis (BSFA) seems to be a useful method to assess the efficiency in energy sector. However, BSFA results do not expose the multiple relationships between input and output variables and energy efficiency. This study proposes a framework to make inferences about BSFA efficiencies, recognizing the underlying relationships between variables and efficiency, using Bayesian network (BN) approach. BN classifiers are proposed as a method to analyze the results obtained from BSFA.     

Two graphical displays for the detection of potentially influential subsets in regression

In the context of the general linear model Y=Xβ+ε, the matrix P_z =Z(Z^TZ)^?1 Z^T , where Z=(X: Y), plays an important role in determining least squares results. In this article we propose two graphical displays for the off-diagonal as well as the diagonal elements of P_Z . The two graphs are based on simple ideas and are useful in the detection of potentially influential subsets of observations in regression. Since P_Z is invariant with respect to permutations of the columns of Z, an added advantage of these graphs is that they can be used to detect outliers in multivariate data where the rows of Z are usually regarded as a random sample from a multivariate population. We also suggest two calibration points, one for the diagonal elements of P_Z and the other for the off-diagonal elements. The advantage of these calibration points is that they take into consideration the variability of the off-diagonal as well as the diagonal elements of P_Z . They also do not suffer from masking.     

The Intervening Role of Innovative Climate: A Study of Middle Managers in Manufacturing Organizations in Iran

Based on a developed model, this study deals with an investigation on whether innovative climate is a variable that moderates the relationship among middle managers' technological innovation and organizational factors such as structure, strategic posture, and environment. We used path analysis framework to study this relationship. The respondents (N=158, n=96) were all the managers of large or medium manufacturing companies with headquarters in the province of Tehran in Iran. The study found that innovative climate has an intervening role in the relationship among organizational structure, strategic posture and environment, and middle managers' technological innovation. The results have implications for the executive and human resource management practices.     

Chaos and Transformation Theories: A Theoretical Analysis with Implications for Organization Theory and Public Management

Chaos and transformation theories have emerged as new currencies in social sciences in general and in systems design and management, and in futuristic studies in particular. This article analyzes chaos and transformation theories in historical and contemporary perspectives, their contributions to social science in general, and organization theory and public management in particular. The notions of chaos and order, change and continuity, and uncertainty and certainty are analyzed along with the growing realization of complexity and non-linear dynamic features of modern organizations and the hard reality of a constant necessity to acquire new knowledge and learn to manage organizations with flexibility and innovation. Finally, the article addresses some of the limitations of chaos theory and outlines a number of implications for organization theory and public management in the age of globalization.     

A Psychological Study of the Inverse Relationship Between Perceived Risk and Perceived Benefit

Judgments of risk and judgments of benefit have been found to be inversely related. Activities or technologies that are judged high in risk tend to be judged low in benefit, and vice versa. In the present study, we examine this inverse relationship in detail, using two measures of relationship between risk and benefit. We find that the inverse relationship is robust and indicative of a confounding of risk and benefit in people's minds. This confounding is linked to a person's overall evaluation of an activity or technology. Theoretical and practical implications of this risk-benefit confounding are discussed.     

Monte Carlo studies of some adaptive robust procedures for location

One linear and two nonlinear adaptive robust procedures have been developed in which preliminary statistics, based on tail lengths, attempt to identify distributions from which the samples arise so that a suitable robust estimator based on trimmed means can be used to estimate the location parameter. The efficiencies of the estimators based on the three proposed adaptive robust procedures have been obtained using Monte Carlo methods involving eight distributions and these efficiencies are compared with the efficiencies of nineteen other robust estimators.