Using the singly truncated normal distribution to analyze satellite data

This paper proposes the singly truncated normal distribution as a model for estimating radiance measurements from satellite-borne infrared sensors. These measurements are made in order to estimate sea surface temperatures which can be related to radiances. Maximum likelihood estimation is used to provide estimates for the unknown parameters. In particular, a procedure is described for estimating clear radiances in the presence of clouds and the Kolmogorov-Smirnov statistic is used to test goodness-of-fit of the measurements to the singly truncated normal distribution. Tables of quantile values of the Kolmogorov-Smirnov statistic for several values of the truncation point are generated from Monie Carlo experiment Mnally a numerical emample using satetic data is presented to illustrate the application of the procedures.     

Tests for the two-sample problem based on empirical probability measures

Tests are proposed for the equality of two unknown distributions. For empirical probability measures that are defined for samples from the two distributions, the proposed tests are based on the supremum of the absolute differences between the corresponding empirical probabilities, the supremum being taken over all possible events (Borel sets). In contrast, competing EDF tests compare only empirical probabilities of a subclass of Borel sets. The proposed tests are compared for simulated samples to the Kolmogorov-Smirnov, Cramér-von Mises, Kuiper, and Mann-Whitney-Wilcoxon tests     

An efficient sequential design of clinical trials

We propose an efficient group sequential monitoring rule for clinical trials. At each interim analysis both efficacy and futility are evaluated through a specified loss structure together with the predicted power. The proposed design is robust to a wide range of priors, and achieves the specified power with a saving of sample size compared to existing adaptive designs. A method is also proposed to obtain a reduced-bias estimator of treatment difference for the proposed design. The new approaches hold great potential for efficiently selecting a more effective treatment in comparative trials. Operating characteristics are evaluated and compared with other group sequential designs in empirical studies. An example is provided to illustrate the application of the method.     

Estimation of covariance matrices based on hierarchical inverse-Wishart priors

This paper focuses on Bayesian shrinkage methods for covariance matrix estimation. We examine posterior properties and frequentist risks of Bayesian estimators based on new hierarchical inverse-Wishart priors. More precisely, we give the conditions for the existence of the posterior distributions. Advantages in terms of numerical simulations of posteriors are shown. A simulation study illustrates the performance of the estimation procedures under three loss functions for relevant sample sizes and various covariance structures.     

Penalized Maximum Likelihood Principle for Choosing Ridge Parameter

We consider the problem of choosing the ridge parameter. Two penalized maximum likelihood (PML) criteria based on a distribution-free and a data-dependent penalty function are proposed. These PML criteria can be considered as “continuous” versions of AIC. A systematic simulation is conducted to compare the suggested criteria to several existing methods. The simulation results strongly support the use of our method. The method is also applied to two real data sets.     

Nonparametric product partition models for multiple change-points analysis

ABSTRACT

We propose an extension of parametric product partition models. We name our proposal nonparametric product partition models because we associate a random measure instead of a parametric kernel to each set within a random partition. Our methodology does not impose any specific form on the marginal distribution of the observations, allowing us to detect shifts of behaviour even when dealing with heavy-tailed or skewed distributions. We propose a suitable loss function and find the partition of the data having minimum expected loss. We then apply our nonparametric procedure to multiple change-point analysis and compare it with PPMs and with other methodologies that have recently appeared in the literature. Also, in the context of missing data, we exploit the product partition structure in order to estimate the distribution function of each missing value, allowing us to detect change points using the loss function mentioned above. Finally, we present applications to financial as well as genetic data.     

Replication: Cheating,loss aversion,and moral attitudes in Vietnam

We use the coin-flip paradigm and a short survey about moral attitudes under three conditions to answer three questions: (i) Do people cheat more when financial incentives are present in comparison with no incentives? (ii) Do they find it more difficult to maintain their ethical standards when they have been given a small amount of money? and (iii) Do moral attitudes predict cheating behavior? Using a sample of Vietnamese college students, we discover that a financial incentive does not matter until people feel that they are facing a loss. In addition, we do not find any evidence that moral attitudes could predict the unethical behavior in our sample. Our findings shed further light on cheating behaviors and loss aversion through an experimental investigation.     

A Note on Estimation of a Distribution Function in a Nonparametric Set-up Using Stein’s Shrinkage Estimation Technique

Based on Stein’s famous shrinkage estimation of a multivariate normal distribution, we propose a new type of estimators of the distribution function of a random variable in a nonparametric setup. The proposed estimators are then compared with the empirical distribution function, which is the best equivariant estimator under a well-known loss function. Our extensive simulation study shows that our proposed estimators can perform better for moderate to large sample sizes.     

交强险的成本因素分析

 内容提要：本文应用聚类分析方法和Beta回归模型对2009年度交强险审计报告所披露的交强险经营数据进行了分析。结果表明,交强险在31个地区的赔付率差异显著,主要影响因素包括人均收入水平、物价指数、人均交通支出和人均车辆数。交强险9个业务类别之间的赔付率差异也很明显,表明现行保费水平存在严重的不公平。各地区的赔付率和费用率之间存在反常的负向相关关系,反映了交强险在费用分摊中存在某种不合理现象。保险公司的经营费用率在不同地区之间和不同业务类别之间都没有显著差异,主要受保险公司业务规模的影响,规模经济效应显著。     

Supplier Evaluation and Selection Based on Stochastic Dominance: A Quality-Based Approach

Quality has become a major business strategy such that organizations with successful improvement of their products quality can gain productivity, enhance market penetration, achieve great profitability, and strongly sustain their competitive advantages. The quality of materials received from suppliers determines not only the quality of assembled products but also satisfaction and loyalty of downstream customers. In this article, we employ decision-making processes of the stochastic dominance on the basis of loss-based capability indices to compare certain potential suppliers. In view of compared results of the first-order and second-order stochastic dominances, each supplier is categorized as a superior supplier, weakly superior supplier, strongly non dominated supplier, or non dominated supplier. We develop a general computational procedure to select the preferable suppliers in an analytical way. To assist decision-makers in selecting preferable suppliers, quantile-quantile plots of loss-based capability indices presenting the results of the first-order stochastic dominance of the indices’ estimators are developed so that they can simultaneously visualize pair-wise comparisons of the suppliers and make appropriate decisions. Finally, a practical example invoking the stochastic dominance using the loss-based capability indices to carry out the quality-based supplier evaluation and selection is presented to demonstrate the applicability of our proposed methodology.