Simplified Likelihood Based Goodness-of-fit Tests for the Weibull Distribution

The aim of this paper is to present new likelihood based goodness-of-fit tests for the two-parameter Weibull distribution. These tests consist in nesting the Weibull distribution in three-parameter generalized Weibull families and testing the value of the third parameter by using the Wald, score, and likelihood ratio procedures. We simplify the usual likelihood based tests by getting rid of the nuisance parameters, using three estimation methods. The proposed tests are not asymptotic. A comprehensive comparison study is presented. Among a large range of possible GOF tests, the best ones are identified. The results depend strongly on the shape of the underlying hazard rate.     

Reference distributions and inequality measurement

We investigate a general problem of comparing pairs of distributions which includes approaches to inequality measurement, the evaluation of “unfair” income inequality, evaluation of inequality relative to norm incomes, and goodness of fit. We show how to represent the generic problem simply using (1) a class of divergence measures derived from a parsimonious set of axioms and (2) alternative types of “reference distributions.” The problems of appropriate statistical implementation are discussed and empirical illustrations of the technique are provided using a variety of reference distributions.     

On the origin of the WTA–WTP divergence in public good valuation

This paper tests whether individual perceptions of markets as good or bad for a public good is correlated with the propensity to report gaps in willingness to pay and willingness to accept revealed within an incentive compatible mechanism. Identifying people based on a notion of market affinity, we find a substantial part of the gap can be explained by controlling for some variables that were not controlled for before. This result suggests the valuation gap for public goods can be reduced through well-defined variables.     

A Sample Selection Model with Skew‐normal Distribution

Non‐random sampling is a source of bias in empirical research. It is common for the outcomes of interest (e.g. wage distribution) to be skewed in the source population. Sometimes, the outcomes are further subjected to sample selection, which is a type of missing data, resulting in partial observability. Thus, methods based on complete cases for skew data are inadequate for the analysis of such data and a general sample selection model is required. Heckman proposed a full maximum likelihood estimation method under the normality assumption for sample selection problems, and parametric and non‐parametric extensions have been proposed. We generalize Heckman selection model to allow for underlying skew‐normal distributions. Finite‐sample performance of the maximum likelihood estimator of the model is studied via simulation. Applications illustrate the strength of the model in capturing spurious skewness in bounded scores, and in modelling data where logarithm transformation could not mitigate the effect of inherent skewness in the outcome variable.     

Exploring the Clustering Effect of the Frailty Survival Model by Means of the Brier Score

In this article, the Brier score is used to investigate the importance of clustering for the frailty survival model. For this purpose, two versions of the Brier score are constructed, i.e., a “conditional Brier score” and a “marginal Brier score.” Both versions of the Brier score show how the clustering effects and the covariate effects affect the predictive ability of the frailty model separately. Using a Bayesian and a likelihood approach, point estimates and 95% credible/confidence intervals are computed. The estimation properties of both procedures are evaluated in an extensive simulation study for both versions of the Brier score. Further, a validation strategy is developed to calculate an internally validated point estimate and credible/confidence interval. The ensemble of the developments is applied to a dental dataset.     

A randomized phase I Bayesian dose escalation design for the combination of anti‐cancer drugs

Nowadays, treatment regimens for cancer often involve a combination of drugs. The determination of the doses of each of the combined drugs in phase I dose escalation studies poses methodological challenges. The most common phase I design, the classic ‘3+3' design, has been criticized for poorly estimating the maximum tolerated dose (MTD) and for treating too many subjects at doses below the MTD. In addition, the classic ‘3+3' is not able to address the challenges posed by combinations of drugs. Here, we assume that a control drug (commonly used and well‐studied) is administered at a fixed dose in combination with a new agent (the experimental drug) of which the appropriate dose has to be determined. We propose a randomized design in which subjects are assigned to the control or to the combination of the control and experimental. The MTD is determined using a model‐based Bayesian technique based on the difference of probability of dose limiting toxicities (DLT) between the control and the combination arm. We show, through a simulation study, that this approach provides better and more accurate estimates of the MTD. We argue that this approach may differentiate between an extreme high probability of DLT observed from the control and a high probability of DLT of the combination. We also report on a fictive (simulation) analysis based on published data of a phase I trial of ifosfamide combined with sunitinib.Copyright © 2014 John Wiley & Sons, Ltd.     

On the distribution of some ordered variables

Abstract

Motivated by a recent article published by Adam and Tawn, we characterize the distribution of two random variables X, Y ordered linearly like X < Y. We suppose that the random variables follow a bivariate extreme value distribution.     

INCOME AND WEALTH CONCENTRATION IN SPAIN FROM A HISTORICAL AND FISCAL PERSPECTIVE

This paper presents series on top shares of income and wealth in Spain using personal income and wealth tax return statistics. Top income shares are highest in the 1930s, fall sharply during the first decade of the Franco dictatorship, then remain stable and low till the 1980s, and have increased since the mid 1990s. The top 0.01% income share in Spain estimated from income tax data is comparable to estimates for the United States and France over the period 1933–1971. Those findings, along with a careful analysis of all published tax statistics, suggest that income tax evasion and avoidance among top income earners in Spain was much less prevalent than previously thought. Wealth concentration has been about stable from 1982 to 2005 as surging real estate prices have benefited the middle class and compensated for a slight increase in financial wealth concentration in the 1990s. We use our wealth series and a simple model to analyze the effects of the wealth tax exemption of stocks for owners‐managers introduced in 1994. We show that the reform induced substantial shifting from the taxable to tax exempt status, hence creating efficiency costs. (JEL D31, H31, O15)     

Too Big to Learn: The Effects of Major Acquisition Failures on Subsequent Acquisition Divestment

We examine whether firms learn from their major acquisition failures. Drawing from a threat‐rigidity theoretical framework, we suggest that firms do not learn from their major acquisition failures. Furthermore, we hypothesize that host‐country experience reinforces the negative effects of major acquisition failures. Our research hypotheses are tested using an event history analysis of 741 acquisitions undertaken by French listed and non‐listed firms in the USA between January 1988 and December 2008. We use failure divestment (divestment resulting from acquisition failure) as a proxy for acquisition performance. Consistent with our theoretical framework, we find that major acquisition failures have a negative impact on future acquisition performance. Furthermore, we find that such negative effects are reinforced by firms’ host‐country experience.     

A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study

Seveso plants are complex sociotechnical systems, which makes it appropriate to support any risk assessment with a model of the system. However, more often than not, this step is only partially addressed, simplified, or avoided in safety reports. At the same time, investigations have shown that the complexity of industrial systems is frequently a factor in accidents, due to interactions between their technical, human, and organizational dimensions. In order to handle both this complexity and changes in the system over time, this article proposes an original and simplified qualitative risk evaluation method based on the system dynamics theory developed by Forrester in the early 1960s. The methodology supports the development of a dynamic risk assessment framework dedicated to industrial activities. It consists of 10 complementary steps grouped into two main activities: system dynamics modeling of the sociotechnical system and risk analysis. This system dynamics risk analysis is applied to a case study of a chemical plant and provides a way to assess the technological and organizational components of safety.