A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil

A heteroscedastic regression based on the odd log-logistic Marshall–Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.     

Sizing Up a Superstorm: Exploring the Role of Recalled Experience and Attribution of Responsibility in Judgments of Future Hurricane Risk

Research suggests that hurricane‐related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals’ distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual‐level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences.     

Label‐ and Level‐Invariant Graphical Log‐Linear Models

We introduce two types of graphical log‐linear models: label‐ and level‐invariant models for triangle‐free graphs. These models generalise symmetry concepts in graphical log‐linear models and provide a tool with which to model symmetry in the discrete case. A label‐invariant model is category‐invariant and is preserved after permuting some of the vertices according to transformations that maintain the graph, whereas a level‐invariant model equates expected frequencies according to a given set of permutations. These new models can both be seen as instances of a new type of graphical log‐linear model termed the restricted graphical log‐linear model, or RGLL, in which equality restrictions on subsets of main effects and first‐order interactions are imposed. Their likelihood equations and graphical representation can be obtained from those derived for the RGLL models.     

Alternative diff-in-diffs estimators with several pretreatment periods

This paper deals with the identification of treatment effects using difference-in-differences estimators when several pretreatment periods are available. We define a family of identifying nonnested assumptions that lead to alternative difference-in-differences estimators. We show that the most usual difference-in-differences estimators imply equivalence conditions for the identifying nonnested assumptions. We further propose a model that can be used to test multiple equivalence conditions without imposing any of them. We conduct a Monte Carlo analysis and apply our approach to several recent papers to show its practical relevance.     

Some Second-Order Asymptotics for Extreme Value Linear Regression Models

ABSTRACT

In this article we derive finite-sample corrections in matrix notation for likelihood ratio and score statistics in extreme-value linear regression models. We consider three corrected score tests that perform better than the usual score test. We also derive general formulae for second-order biases of maximum likelihood estimates of the linear parameters. Some simulations are performed to compare the likelihood ratio and score statistics with their modified versions and to illustrate the bias correction.     

Generalised Variance Function Estimation for Binary Variables in Large‐Scale Sample Surveys

Generalised variance function (GVF) models are data analysis techniques often used in large‐scale sample surveys to approximate the design variance of point estimators for population means and proportions. Some potential advantages of the GVF approach include operational simplicity, more stable sampling errors estimates and providing a convenient method of summarising results when a high number of survey variables is considered. In this paper, several parametric and nonparametric methods for GVF estimation with binary variables are proposed and compared. The behavior of these estimators is analysed under heteroscedasticity and in the presence of outliers and influential observations. An empirical study based on the annual survey of living conditions in Galicia (a region in the northwest of Spain) illustrates the behaviour of the proposed estimators.     

Cambodia: Reconstructing the Demographic Stab of the Past and Forecasting the Demographic Scar of the Future

Cambodia went through 5 years of violent internal conflicts (1970–1975), including the spread of the American–Vietnamese war into the country, followed by four more years of brutal and chaotic Khmer Rouge government (1975–1979). The result was almost 2 million deaths. This article attempts to contribute to the reconstruction of the demographic history of Cambodia. This is done in three ways. First, by analysing scars in the pyramids corresponding to the recent past, present and future; second, by estimating major demographic events during the 1970s, especially excess deaths; and third, by comparing the size and composition of a simulated normal population with the real population.     

A New Weibull–Pareto Distribution: Properties and Applications

Many distributions have been used as lifetime models. In this article, we propose a new three-parameter Weibull–Pareto distribution, which can produce the most important hazard rate shapes, namely, constant, increasing, decreasing, bathtub, and upsidedown bathtub. Various structural properties of the new distribution are derived including explicit expressions for the moments and incomplete moments, Bonferroni and Lorenz curves, mean deviations, mean residual life, mean waiting time, and generating and quantile functions. The Rényi and q entropies are also derived. We obtain the density function of the order statistics and their moments. The model parameters are estimated by maximum likelihood and the observed information matrix is determined. The usefulness of the new model is illustrated by means of two real datasets on Wheaton river flood and bladder cancer. In the two applications, the new model provides better fits than the Kumaraswamy–Pareto, beta-exponentiated Pareto, beta-Pareto, exponentiated Pareto, and Pareto models.     

Bartlett-corrected tests for normal linear models when the error covariance matrix is nonscaiar

This paper provides Bartlett corrections to improve likelihood ratio tests for heteroskedastic normal linear models when the error covariance matrix is nonscaiar and depends on a set of unknown parameters. The Bartlett corrections are simple enough to be used algebraically to obtain several closed-form expressions in special cases. The corrections have also advantages for numerical purposes because they involve only simple operations on matrices and vectors.