On characterizations and variance bounds of discrete α-unimodality

Characterizations of α-unimodality for integer-valued random variables about a specific mode are established in terms of their probability mass functions, distribution functions and characteristic functions. Using these characterizations variance lower bounds in terms of α and the mode are derived. For α=1 all these results are reduced to ordinary unimodality. The new variance lower bounds for discrete unimodality is sharper than its continuous counterpart. An upper bound for the variance of discrete unimodal distribution defined on a finite support is discussed.     

Public Organizations in the Age of Accelerated Globalization

Public Organization Review -     

An extended Lindley distribution

In this paper we introduce an extension of the Lindley distribution which offers a more flexible model for lifetime data. Several statistical properties of the distribution are explored, such as the density, (reversed) failure rate, (reversed) mean residual lifetime, moments, order statistics, Bonferroni and Lorenz curves. Estimation using the maximum likelihood and inference of a random sample from the distribution are investigated. A real data application illustrates the performance of the distribution.     

A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance

Compliance with one specified dosing strategy of assigned treatments is a common problem in randomized drug clinical trials. Recently, there has been much interest in methods used for analysing treatment effects in randomized clinical trials that are subject to non-compliance. In this paper, we estimate and compare treatment effects based on the Grizzle model (GM) (ignorable non-compliance) as the custom model and the generalized Grizzle model (GGM) (non-ignorable non-compliance) as the new model. A real data set based on the treatment of knee osteoarthritis is used to compare these models. The results based on the likelihood ratio statistics and simulation study show the advantage of the proposed model (GGM) over the custom model (GGM).     

Hurricane Katrina, the Crisis of Leadership, and Chaos Management: Time for Trying the ‘Surprise Management Theory in Action’

Four years have gone by since the historic Hurricane Katrina hit and drowned the city of New Orleans and caused a massive crisis of, and a global case of grand failure in, governance, leadership, and public management. Advancing on an earlier work published in Public Administration Review (Farazmand 2007), in which a global case of grand failure was established with several lessons drawn for future crisis management, this article argues further for developing and applying a theory of ‘surprise management’ to manage future crises and chaotic situations. Crises are borne out of natural and human made disasters, catastrophes, revolutions, and rapidly changing emergencies. Surprise management is the best approach to managing or coping with crises and crisis driven emergencies.     

Promoting Public Trust in Public Organizations: Explaining the Role of Public Accountability

This paper is aimed at explaining the role of public accountability in promoting public trust in public organizations. Initially a conceptual model was developed. Then, the model was tested empirically in Iran. The result shows that public accountability influences the public trust by improving citizens’ satisfaction. In other words, legal, ethical, financial, functional, and utmost political accountability affect public trust through influencing citizens’ satisfaction. This study also confirms that changes in social trust, citizens’ trust in government, and media will moderate trust in public organizations.

Procedures for the identification of multiple influential observations in linear regression

Since the seminal paper by Cook (1977) in which he introduced Cook's distance, the identification of influential observations has received a great deal of interest and extensive investigation in linear regression. It is well documented that most of the popular diagnostic measures that are based on single-case deletion can mislead the analysis in the presence of multiple influential observations because of the well-known masking and/or swamping phenomena. Atkinson (1981) proposed a modification of Cook's distance. In this paper we propose a further modification of the Cook's distance for the identification of a single influential observation. We then propose new measures for the identification of multiple influential observations, which are not affected by the masking and swamping problems. The efficiency of the new statistics is presented through several well-known data sets and a simulation study.     

Failure Time of Non Homogeneous Gamma Process

We consider the non homogeneous gamma process as a degradation model. The hitting time of a deterministic or random level is studied here. We provide its distribution (both cdf and pdf) explicitly in the first case and in the second case when the threshold is exponentially or gamma distributed. The general case for a random threshold can be approximated by considering mixtures of Erlang distributions. Aging properties are also discussed in this article.