A Goodness of Fit Test for the Multilevel Logistic Model

It is crucial to test the goodness of fit of a model before it is used to make statistical inferences. However, no satisfactory goodness of fit test is available for the case of categorical multilevel data which occur when categorical data are clustered or hierarchical in nature. Hence the aim of this paper is to develop a new goodness of fit test for multilevel binary data based on Hosmer and Lemeshow and Lipsitz et.al. In order to identify the properties of the developed test, simulation studies were carried out to assess the Type I error and the power.     

A two-sample test for threshold crossing latent variables with application to valuation studies

Summary.  Contingent valuation researchers are often interested in a comparison of the underlying willingness-to-pay distributions in two independent studies. Since willingness to pay is not observable, traditional testing procedures for comparing distributions cannot be applied directly. The paper proposes a permutation test for this sort of comparison. The main distinguishing characteristic of the test proposed is that it does not rely on asymptotic approximations and facilitates the introduction of covariates. The permutation test is illustrated with the case of projects of investment for the improvement of two important Brazilian river basins.     

Binary Mixtures of Polycyclic Aromatic Hydrocarbons Display Nonadditive Mixture Interactions in an In Vitro Liver Cell Model

Polycyclic aromatic hydrocarbons (PAHs) have been labeled contaminants of concern due to their carcinogenic potential, insufficient toxicological data, environmental ubiquity, and inconsistencies in the composition of environmental mixtures. The Environmental Protection Agency is reevaluating current methods for assessing the toxicity of PAHs, including the assumption of toxic additivity in mixtures. This study was aimed at testing mixture interactions through in vitro cell culture experimentation, and modeling the toxicity using quantitative structure‐activity relationships (QSAR). Clone‐9 rat liver cells were used to analyze cellular proliferation, viability, and genotoxicity of 15 PAHs in single doses and binary mixtures. Tests revealed that many mixtures have nonadditive toxicity, but display varying mixture effects depending on the mixture composition. Many mixtures displayed antagonism, similar to other published studies. QSARs were then developed using the genetic function approximation algorithm to predict toxic activity both in single PAH congeners and in binary mixtures. Effective concentrations inhibiting 50% of the cell populations were modeled, with R² = 0.90, 0.99, and 0.84, respectively. The QSAR mixture algorithms were then adjusted to account for the observed mixture interactions as well as the mixture composition (ratios) to assess the feasibility of QSARs for mixtures. Based on these results, toxic addition is improbable and therefore environmental PAH mixtures are likely to see nonadditive responses when complex interactions occur between components. Furthermore, QSAR may be a useful tool to help bridge these data gaps surrounding the assessment of human health risks that are associated with PAH exposures.     

Some robust design strategies for percentile estimation in binary response models

For the problem of percentile estimation of a quantal response curve, the authors determine multiobjective designs which are robust with respect to misspecifications of the model assumptions. They propose a maximin approach based on efficiencies which leads to designs that are simultaneously efficient with respect to various choices of link functions and parameter regions. Furthermore, the authors deal with the problems of designing model and percentile robust experiments. They give various examples of such designs, which are calculated numerically.     

Robust Estimation in Binary Choice Models

The binary-response smoothed maximum score (SMS) estimator accommodates heteroskedasticity of an unknown form, but it may be heavily biased when the conditional error density is not differentiable or not bell shaped. We construct a new combined SMS estimator as a linear combination of individual estimators with weights chosen to minimize the trace of estimated mean squared error. This estimator is robust and rate-adaptive under weak assumptions on the density. Results of a Monte Carlo study confirm good performance of the combined estimator.     

Using the Canonical Design Space to Obtain c-optimal Designs for the Quadratic Logistic Model

c-optimal designs for estimating the model parameters of the quadratic logistic regression model are considered. The designs are constructed via the canonical design space. It is shown that the number of design points varies between 1 and 4 depending on the parameter being estimated. Furthermore, formulae for finding the design points along with the corresponding design weights are derived.     

带异质线性趋势的动态二元选择面板模型估计

本文提出了带异质线性趋势的动态二元面板模型的极大似然偏误纠正估计量和近似条件Logit估计量。我们给出了通常极大似然估计量偏误的解析形式,并提供了相应的估计方法。小样本实验表明近似条件似然函数可以很好的消除异质性参数的影响,而偏误纠正估计量可以显著的修正极大似然估计量的偏误。最后我们将本文提出的方法应用到现金红利支付模型。     

Exact Inference for a Population Proportion Based on a Ranked Set Sample

ABSTRACT

This article develops and investigates a confidence interval and hypothesis testing procedure for a population proportion based on a ranked set sample (RSS). The inference is exact, in the sense that it is based on the exact distribution of the total number of successes observed in the RSS. Furthermore, this distribution can be readily computed with the well-known and freely available R statistical software package. A data example that illustrates the methodology is presented. In addition, the properties of the inference procedures are compared with their simple random sample (SRS) counterparts. In regards to expected lengths of confidence intervals and the power of tests, the RSS inference procedures are superior to the SRS methods.     

Systematically misclassified binary dependent variables

ABSTRACT

When a binary dependent variable is misclassified, that is, recorded in the category other than where it really belongs, probit and logit estimates are biased and inconsistent. In some cases, the probability of misclassification may vary systematically with covariates, and thus be endogenous. In this paper, we develop an estimation approach that corrects for endogenous misclassification, validate our approach using a simulation study, and apply it to the analysis of a treatment program designed to improve family dynamics. Our results show that endogenous misclassification could lead to potentially incorrect conclusions unless corrected using an appropriate technique.     

Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

The multi-cycle organization of modern university systems stimulates the interest in studying the progression to higher level degree courses during the academic career. In particular, after the achievement of the first level qualification (Bachelor degree), students have to decide whether to continue their university studies, by enrolling in a second level (Master) programme, or to conclude their training experience. In this work we propose a binary quantile regression (BQR) approach to analyse the Bachelor-to-Master transition phenomenon with the adoption of the Bayesian inferential perspective. In addition to the traditional predictors of academic outcomes, such as the personal characteristics and the field of study, different aspects of student's performance are considered. Moreover, the role of a new contextual variable, representing the type of university regulations experienced during the academic path, is investigated. The utility of the Bayesian BQR to characterize the non-continuation decision after the first cycle studies is illustrated with an application to administrative data of Bachelor graduates at the School of Economics of Sapienza University of Rome. The method favourably compares with more conventional model specifications concerning the conditional mean of the binary response.