历史分析与比较政治

本文讨论了比较政治学中历史研究的主要分析方法.第一类是因果分析法,包括横截面分析法和样本内分析法.第二类是时序分析法,包括对路径依赖和发展顺序的分析.本文认为,历史分析与众不同的分析方法使它成为比较政治研究中有力的分析工具.     

Teaching Manual Signs to Adults With Mental Retardation Using Matching-to-Sample Procedures and Stimulus Equivalence

The objective of this study was to teach manual signs through an automated matching-to-sample procedure and to test for the emergence of new conditional relations and imitative behaviors. Seven adults with mild to severe mental retardation participated. Four were also hearing impaired. Relations between manual signs (set A) and pictures (set B) were initially taught, followed by the training of corresponding printed words (set C) and pictures (set B). Further presentations of conditional discriminations tested for the emergence of AC, followed by tests for the emergence of imitative signing behavior (D) in the presence of either pictures (B) or printed words (C). Each stimulus set was comprised of 9 elements. The stimuli were still pictures, printed words, and dynamic presentations of manual signs. A pretest was conducted to determine which signs the participants could make pre-experimentally. Teaching was arranged in a multiple baseline design across 3 groups of 3 words each. The purpose of the present study was to determine whether participants would emit manual signs in expressive signs tests as a result of observation (video modeling) during match-to-sample training in the absence of explicit training. Five of the 7 subjects passed tests of emergence and emitted at least 50% of the signs. Two were hearing impaired with signing experience, and 3 were not hearing impaired and had no signing experience. Thus, observation of video recorded manual signs in a matching-to-sample training procedure was effective at establishing some signs by adults with mental retardation.     

Influence diagnostics for Student-t censored linear regression models

In this paper, we extend the censored linear regression model with normal errors to Student-t errors. A simple EM-type algorithm for iteratively computing maximum-likelihood estimates of the parameters is presented. To examine the performance of the proposed model, case-deletion and local influence techniques are developed to show its robust aspect against outlying and influential observations. This is done by the analysis of the sensitivity of the EM estimates under some usual perturbation schemes in the model or data and by inspecting some proposed diagnostic graphics. The efficacy of the method is verified through the analysis of simulated data sets and modelling a real data set first analysed under normal errors. The proposed algorithm and methods are implemented in the R package CensRegMod.     

Bayesian analysis of skew-normal independent linear mixed models with heterogeneity in the random-effects population

We present a new class of models to fit longitudinal data, obtained with a suitable modification of the classical linear mixed-effects model. For each sample unit, the joint distribution of the random effect and the random error is a finite mixture of scale mixtures of multivariate skew-normal distributions. This extension allows us to model the data in a more flexible way, taking into account skewness, multimodality and discrepant observations at the same time. The scale mixtures of skew-normal form an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal, skew-Student-t, skew-slash and the skew-contaminated normal distributions as special cases, being a flexible alternative to the use of the corresponding symmetric distributions in this type of models. A simple efficient MCMC Gibbs-type algorithm for posterior Bayesian inference is employed. In order to illustrate the usefulness of the proposed methodology, two artificial and two real data sets are analyzed.     

Further evidence of automatic reinforcement effects on verbal form

The Analysis of Verbal Behavior - The form of a verbal response allows for reinforcement mediation and language transmission across cultures. Reinforcement, in turn, plays a decisive role in...     

A non-iterative sampling Bayesian method for linear mixed models with normal independent distributions

In this paper, we utilize normal/independent (NI) distributions as a tool for robust modeling of linear mixed models (LMM) under a Bayesian paradigm. The purpose is to develop a non-iterative sampling method to obtain i.i.d. samples approximately from the observed posterior distribution by combining the inverse Bayes formulae, sampling/importance resampling and posterior mode estimates from the expectation maximization algorithm to LMMs with NI distributions, as suggested by Tan et al. [33 Tan, M., Tian, G. and Ng, K. 2003. A noniterative sampling method for computing posteriors in the structure of EM-type algorithms. Statist. Sinica, 13(3): 625–640. [Web of Science ®] , [Google Scholar]]. The proposed algorithm provides a novel alternative to perfect sampling and eliminates the convergence problems of Markov chain Monte Carlo methods. In order to examine the robust aspects of the NI class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback–Leibler divergence. Further, some discussions on model selection criteria are given. The new methodologies are exemplified through a real data set, illustrating the usefulness of the proposed methodology.     

Multivariate measurement error models based on Student-t distribution under censored responses

Measurement error models constitute a wide class of models that include linear and nonlinear regression models. They are very useful to model many real-life phenomena, particularly in the medical and biological areas. The great advantage of these models is that, in some sense, they can be represented as mixed effects models, allowing us to implement well-known techniques, like the EM-algorithm for the parameter estimation. In this paper, we consider a class of multivariate measurement error models where the observed response and/or covariate are not fully observed, i.e., the observations are subject to certain threshold values below or above which the measurements are not quantifiable. Consequently, these observations are considered censored. We assume a Student-t distribution for the unobserved true values of the mismeasured covariate and the error term of the model, providing a robust alternative for parameter estimation. Our approach relies on a likelihood-based inference using an EM-type algorithm. The proposed method is illustrated through some simulation studies and the analysis of an AIDS clinical trial dataset.     

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind‐related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.     

Species richness and seasonality of spiders (Arachnida, Araneae) in an urban Atlantic Forest fragment in Northeastern Brazil

Species richness and forest seasonality of spiders in an urban fragment of Northeastern Brazil were investigated. Mata do Buraquinho is a remnant of Atlantic Rain Forest where no spider inventories have ever been made. Samplings during dry and wet seasons were carried out in order to establish if seasonality influences species richness and complementarily of the spider fauna. Pitfall traps, nocturnal collections, and beating tray samples were used to access a multi-guild spider fauna. Spider species were estimated using first order Jackknife method. Four hundred samples were obtained, resulting in 1681 adult spiders. A total of 166 species were recorded and 211 were estimated. No significant differences for species richness were observed between both periods, and the captured species were the same according to the complementarily coefficients. The fragmentation hypothesis as factor of biota impoverishment was not tested, but future investigations on this topic are suggested in the complex of forest remnants in João Pessoa.