Maximal inequalities for partial sums of independent random vectors with multi dimensional time parameters

We obtain upper and lower bounds on the distribution of the partial sums constructed from a multi-dimensional array of independent random vectors. These bounds include, among others, generalizations of some of the well known classical inequalities such as the converse Kolmogorov and the Skorokhod-Ottaviani maximal inequalities.     

On the moment generating functions for integer valued random variables

Abstract

In this note, we give explicit expressions of moment generating functions for integer valued random variables in both univariate and multivariate cases, which extend the results obtained by Nadarajah and Mitov [Communications in Statistics–Theory and Methods, 32, 2003, 47–60] and more recently by Chakraborti, Jardim and Epprecht [The American Statistician, 2017], Kwong and Nadarajah [Communications in Statistics–Theory and Methods, 2017]. Some examples are also discussed.     

Adaptive Estimation of the Integral of Squared Regression Derivatives

A problem of estimating the integral of a squared regression function and of its squared derivatives has been addressed in a number of papers. For the case of a heteroscedastic model where smoothness of the underlying regression function, the design density, and the variance of errors are known, the asymptotically sharp minimax lower bound and a sharp estimator were found in Pastuchova & Khasminski (1989). However, there are apparently no results on the either rate optimal or sharp optimal adaptive, or data-driven, estimation when neither the degree of regression function smoothness nor design density, scale function and distribution of errors are known. After a brief review of main developments in non-parametric estimation of non-linear functionals, we suggest a simple adaptive estimator for the integral of a squared regression function and its derivatives and prove that it is sharp-optimal whenever the estimated derivative is sufficiently smooth.     

FPK法在集总参数系统随机温度研究中的应用

应用FPK法研究了集总参数系统在对流和辐射环境下温度随机变化的规律。设激励是均值为0的白噪声且系统参数为常量，在与环境对流换热时，系统随机温度的均值不变，方差与系统时间常数的平方根成反比；在与环境辐射换热时，系统随机温度一般不呈正态分布，其中均值，均方值还与系统确定性温度有关，由此可知，当存在随机激励时，用有热惯性的测温系统精确测定动态温度是不可能的。     

随机-无标度统一混合演化网络模型

根据实际网络的特点,提出了一个随机-无标度统一混合演化网络模型.通过引入一个调节参数p将随机演化网络与(Barabási-Albert)无标度网络统一起来.理论计算和计算机模拟表明,当调节参数p从1变化到0时,网络结构实现了从随机演化网络到BA无标度网络的连续过渡.     

关于岩石破碎的三角形效应研究

本文从分形几何学的角度研究了岩石的断裂规律，从理论上论述了岩石破碎的三角形效应，并给出了一般岩石破碎的数学模型．     

物流外包:从随机结盟到战略联盟

该文在比较随机结盟的物流外包与战略联盟的物流外包本质区别后,提出通过建立恰当的契约机制可以实现从随机结盟的物流外包向战略联盟的物流外包飞跃的观点。在总结物流外包契约机制类型的基础上,运用博弈双方信息是否对称以及博弈方横向之间是否存在竞争性关联等两个维度,构建了物流外包契约机制体系的理论模型,分析了不同物流外包博弈结构契约机制的决策机理,并指出了相应的契约决策条款及契约机制类别。     

一种定量敏感性问题的随机化调查方法

在敏感性问题的随机化调查中,既有调查个体是否具有敏感属性的二值敏感性问题,即属性特征问题,又有调查特定人群中敏感指标量的“多少”问题,即定量特征敏感性问题.对于定量特征敏感性问题的调查,E ichhorn,H aryre于1983年提出了一种随机化调查方法,并给出了定量敏感问题指标量均值的一个无偏估计.本文提出一种定量特征敏感性问题的随机化调查方法,得到的结果表明,本文提出的方法优于E ichhorn,H aryre(1983)提出的方法.     

Bootstrapping data with multiple levels of variation

The authors consider general estimators for the mean and variance parameters in the random effect model and in the transformation model for data with multiple levels of variation. They show that these estimators have different distributions under the two models unless all the variables have Gaussian distributions. They investigate the asymptotic properties of bootstrap procedures designed for the two models. They also report simulation results and illustrate the bootstraps using data on red spruce trees.     

A Mixed Model Approach for Geoadditive Hazard Regression

Abstract.  Mixed model based approaches for semiparametric regression have gained much interest in recent years, both in theory and application. They provide a unified and modular framework for penalized likelihood and closely related empirical Bayes inference. In this article, we develop mixed model methodology for a broad class of Cox-type hazard regression models where the usual linear predictor is generalized to a geoadditive predictor incorporating non-parametric terms for the (log-)baseline hazard rate, time-varying coefficients and non-linear effects of continuous covariates, a spatial component, and additional cluster-specific frailties. Non-linear and time-varying effects are modelled through penalized splines, while spatial components are treated as correlated random effects following either a Markov random field or a stationary Gaussian random field prior. Generalizing existing mixed model methodology, inference is derived using penalized likelihood for regression coefficients and (approximate) marginal likelihood for smoothing parameters. In a simulation we study the performance of the proposed method, in particular comparing it with its fully Bayesian counterpart using Markov chain Monte Carlo methodology, and complement the results by some asymptotic considerations. As an application, we analyse leukaemia survival data from northwest England.