A Graphical Tool for Interpreting Regression Coefficients of Trinomial Logit Models

Multinomial logit (also termed multi-logit) models permit the analysis of the statistical relation between a categorical response variable and a set of explicative variables (called covariates or regressors). Although multinomial logit is widely used in both the social and economic sciences, the interpretation of regression coefficients may be tricky, as the effect of covariates on the probability distribution of the response variable is nonconstant and difficult to quantify. The ternary plots illustrated in this article aim at facilitating the interpretation of regression coefficients and permit the effect of covariates (either singularly or jointly considered) on the probability distribution of the dependent variable to be quantified. Ternary plots can be drawn both for ordered and for unordered categorical dependent variables, when the number of possible outcomes equals three (trinomial response variable); these plots allow not only to represent the covariate effects over the whole parameter space of the dependent variable but also to compare the covariate effects of any given individual profile. The method is illustrated and discussed through analysis of a dataset concerning the transition of master’s graduates of the University of Trento (Italy) from university to employment.     

Visualization of generalized mean estimators using auxiliary information in survey sampling

Abstract

The mean estimators with ratio depend on multiple auxiliary variables and unknown parameters in a finite population setting. We propose a new generalized approach with matrices for modeling the mutivariate mean estimators with two auxiliary variables. Our approach brings naturally a graphical analysis for comparing mean estimators.     

On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.     

Empirical likelihood confidence intervals for the mean of a population containing many zero values

If a population contains many zero values and the sample size is not very large, the traditional normal approximation‐based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. In the context of survey sampling, however, there is a general preference for making minimal assumptions about the population under study. The authors have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. They show that under a variety of hypothetical populations, these intervals often outperformed parametric likelihood intervals by having more balanced coverage rates and larger lower bounds. The authors illustrate their methodology using data from the Canadian Labour Force Survey for the year 2000.     

面孔认知中表情的印象操作的有效性

运用促进面孔记忆新的处理策略“表情的印象操作”，研究在笑脸中包含着怎样的信息。研究发现，与平静表情的面孔相比，笑脸是更具优势的表情，在笑脸的表情之中包含着更多促进面孔记忆的信息。     

A Note on the Role of the Propensity Score for Estimating Average Treatment Effects

Hahn [Hahn, J. (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica 66:315-331] derived the semiparametric efficiency bounds for estimating the average treatment effect (ATE) and the average treatment effect on the treated (ATET). The variance of ATET depends on whether the propensity score is known or unknown. Hahn attributes this to “dimension reduction.” In this paper, an alternative explanation is given: Knowledge of the propensity score improves upon the estimation of the distribution of the confounding variables.     

浅论缩短反馈时间在中学英语写作教学中的作用

本文通过调查一所普通中学学生对教师英语作文反馈时间的偏好并进行实验研究,利用统计学的描述性统计,独立/相关样本T检验分析方法,作者发现缩短反馈时间确实能够提高学生的英语写作水平,并结合其对教学的启示进行了讨论。     

双波长比值法测定氯硝基苯异构体的含量

依据邻、间、对氯硝基苯三组分的比值光谱特征，以间氟硝基苯为参比组分，选择219nm、232nm和244nm、297nm作为邻氯硝基苯和对氯硝基苯的测定波长；以对氯硝基苯为参比组分．选择253nm、325nm作为间氟硝基苯的测定波长．测定邻、间、对氯硝基苯混合样品中三组分含量，结果显示。邻氯硝基苯在（3．73～74．34）mg／L，间氯硝基苯在（1．58～105．1）mg／L，对氯硝基苯在（0．36-107．1）mg／L范围具有良好的线性关系．回收率均在95．0％～110．0％之间．本法具有测定波长少、计算简单、光谱“分离”能力强。易于推广等特点．     

南京国民政府时期南洋侨教政策负面影响述评

由于历史和现实的缘故，国民政府十分关注海外华侨尤其南洋华侨的教育，形成了比较完善的华侨教育体系，学校和学生数量呈上升趋势，学生素质也不断提高。然而由于国民政府侨教政策自身固有的弊病，给南洋侨教带来了相当的消极作用，限制了南洋侨教事业的进一步发展。     

Adaptive tests of regression functions via multiscale generalized likelihood ratios

Many applications of nonparametric tests based on curve estimation involve selecting a smoothing parameter. The author proposes an adaptive test that combines several generalized likelihood ratio tests in order to get power performance nearly equal to whichever of the component tests is best. She derives the asymptotic joint distribution of the component tests and that of the proposed test under the null hypothesis. She also develops a simple method of selecting the smoothing parameters for the proposed test and presents two approximate methods for obtaining its P‐value. Finally, she evaluates the proposed test through simulations and illustrates its application to a set of real data.