Fuzzy值连续函数与Fuzzy值函数的序列收敛

利用Ｆｕｚｚｙ数的Ｆｕｚｚｙ距离定义了Ｆｕｚｚｙ值连续函数的序列收敛，证明了Ｆｕｚｚｙ值连续函数与Ｆｕｚｚｙ值连续函数的和差仍是Ｆｕｚｚｙ值连续函数；如果Ｆｕｚｚｙ值函数序列的每项Ｆｕｚｚｙ值连续，则其极限也Ｆｕｚｚｙ值连续；在一致连续的条件下，积分号和极限号、微分号和极限号可交换．     

Sur la convergence des tests de Schlee et de Yatchew

L'auteur adapte l'approche de Schlee (1980) et de Yatchew (1992) à la construction de deux tests de convexité pour une fonction de régression dans un modèle non paramétrique. Il établit de nouveaux résultats de convergence pour ces tests et étudie leur comportement dans de petits échantillons à l'aide de simulations.     

减缓渐开线齿轮支座振动的有效措施

在齿轮传动领域中，至今仍然普遍认为，只要主动齿轮的输入扭矩保持恒定，渐开线齿轮支座就不会产生振动。本文的研究充分表明，渐开线齿轮机的构运行时，它的支座不可避免地会发生一定程度的振动，作者对渐开线齿轮机构运行过程中支座实际反力变化规律作了系统的剖析及研究，并推导出渐开线齿轮支座实际反力计算式及支座实际反力波动率方程组。该齿轮支座实际反力波动率方程组所显示的严密的数学力学关系揭示了导致渐开线齿轮支座强迫振动的主要隐因素就是齿间滑动摩擦，深入的分析研究进一步表明，抑制齿轮支座强迫振动的重要措施是有效地降低齿间滑动摩擦系数、合理地调配齿数和传动化比以及严格地控制中心距增量系数。     

On some sampling distributions for skew-normal population

The distribution of weighted function of independent skew-normal random variables, which includes the sample mean, is useful in many applications. In this paper, we derive this distribution and study the null distribution of a linear form and a quadratic form. Finally, we discuss some of its applications in control charts, in which the skew-normal model plays a key role.     

Model selection and parameter estimation of a multinomial logistic regression model

In the multinomial regression model, we consider the methodology for simultaneous model selection and parameter estimation by using the shrinkage and LASSO (least absolute shrinkage and selection operation) [R. Tibshirani, Regression shrinkage and selection via the LASSO, J. R. Statist. Soc. Ser. B 58 (1996), pp. 267–288] strategies. The shrinkage estimators (SEs) provide significant improvement over their classical counterparts in the case where some of the predictors may or may not be active for the response of interest. The asymptotic properties of the SEs are developed using the notion of asymptotic distributional risk. We then compare the relative performance of the LASSO estimator with two SEs in terms of simulated relative efficiency. A simulation study shows that the shrinkage and LASSO estimators dominate the full model estimator. Further, both SEs perform better than the LASSO estimators when there are many inactive predictors in the model. A real-life data set is used to illustrate the suggested shrinkage and LASSO estimators.     

Bayesian Analysis of Different Hybrid and Progressive Life Tests

Type-I and Type-II censoring schemes are the widely used censoring schemes available for life testing experiments. A mixture of Type-I and Type-II censoring schemes is known as a hybrid censoring scheme. Different hybrid censoring schemes have been introduced in recent years. In the last few years, a progressive censoring scheme has also received considerable attention. In this article, we mainly consider the Bayesian inference of the unknown parameters of two-parameter exponential distribution under different hybrid and progressive censoring schemes. It is observed that in general the Bayes estimate and the associated credible interval of any function of the unknown parameters, cannot be obtained in explicit form. We propose to use the Monte Carlo sampling procedure to compute the Bayes estimate and also to construct the associated credible interval. Monte Carlo Simulation experiments have been performed to see the effectiveness of the proposed method in case of Type-I hybrid censored samples. The performances are quite satisfactory. One data analysis has been performed for illustrative purposes.     

Another Tale of Two Tails: On Characterizations of Comparative Risk

We characterize the classes of utility functions that are consistent with different notions of mean preserving spreads introduced in the literature. This gives rise to a unified approach and extension of some definitions of increasing risk, including the concepts of Rothschild and Stiglitz (1970) and Landsberger and Meilijson (1990a,b). The main idea is to restrict the centers of the mean preserving spreads to an arbitrary subset.     

Good ridge estimators based on prior information

Ridge regression is re-examined and ridge estimators based on prior information are introduced. A necessary and sufficient condition is given for such ridge estimators to yield estimators of every nonnull linear combination of the regression coefficients with smaller mean square error than that of the Gauss-Markov best linear unbiased estimator.     

Critical Bellman–Harris branching processes with infinite variance allowing state-dependent immigration

The branching processes with state-dependent immigration are considered as alternating regenerative processes. The main purpose is to demonstrate some new “regenerative” methods. Critical Bellman–Harris branching processes with state-dependent immigration are investigated and new limit theorems are obtained in the case of an infinite offspring variance and possibly infinite mean of the immigrants.     

Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t-Statistic

ABSTRACT

This article is concerned with inference in the linear model with dyadic data. Dyadic data are indexed by pairs of “units;” for example, trade data between pairs of countries. Because of the potential for observations with a unit in common to be correlated, standard inference procedures may not perform as expected. We establish a range of conditions under which a t-statistic with the dyadic-robust variance estimator of Fafchamps and Gubert is asymptotically normal. Using our theoretical results as a guide, we perform a simulation exercise to study the validity of the normal approximation, as well as the performance of a novel finite-sample correction. We conclude with guidelines for applied researchers wishing to use the dyadic-robust estimator for inference.