Strong law of large numbers and complete convergence for non-identically distributed WOD random variables

In this paper, we establish the strong law of large numbers and complete convergence for non-identically distributed WOD random variables. We derive some new inequalities of Fuk–Nagaev type for the sums of non-identically distributed WD random variables. All these results further extend and refine previous ones.     

Modeling spatial overdispersion via the generalized Waring point process

Modeling spatial overdispersion requires point process models with finite‐dimensional distributions that are overdisperse relative to the Poisson distribution. Fitting such models usually heavily relies on the properties of stationarity, ergodicity, and orderliness. In addition, although processes based on negative binomial finite‐dimensional distributions have been widely considered, they typically fail to simultaneously satisfy the three required properties for fitting. Indeed, it has been conjectured by Diggle and Milne that no negative binomial model can satisfy all three properties. In light of this, we change perspective and construct a new process based on a different overdisperse count model, namely, the generalized Waring (GW) distribution. While comparably tractable and flexible to negative binomial processes, the GW process is shown to possess all required properties and additionally span the negative binomial and Poisson processes as limiting cases. In this sense, the GW process provides an approximate resolution to the conundrum highlighted by Diggle and Milne.     

基于投入产出非线性优化的制造业完全能耗强度情景分析

当前,环境污染仍为中国最突出的问题之一,而降低制造业完全能耗强度是减少消耗及污染的重要途径。本文基于投入产出非线性优化理论,以制造业完全能耗强度最小为目标,并以18个行业最终需求为决策变量,构建了一个完全能耗强度非线性优化模型,特引入了进出口系数等约束条件,并基于已有投入产出表等数据,预测调整模型的相关系数。在此基础上,设计了三个方案与两个情景,并运用粒子群算法求解模型。结果表明:2015年中国制造业完全能耗强度最小值区间为0.7823-0.9048tce/万元,相比2010年,下降率区间为8.9%-21.31%。在高方案情景2下,可实现制造业完全能耗强度降低20%的目标。为实现该目标,应促进中低能耗制造业发展、降低高能耗制造业的完全能耗量、适当提高消费和进口系数并降低投资和出口系数。本文既有利于政府部门制定科学系统的节能减排政策,也有利于深化能源经济与管理理论。     

On Complete Convergence for an Extended Negatively Dependent Sequence

In this article, the Rosenthal-type maximal inequality for extended negatively dependent (END) sequence is provided. By using the Rosenthal type inequality, we present some results of complete convergence for weighted sums of END random variables under mild conditions.     

Statistical Analysis of Parameter Estimation for 2-D Harmonics in Multiplicative and Additive Noise

This article considers the problem of parameter estimation for two dimensional (2-D) multi-component harmonics in non zero-mean multiplicative and additive noise. The least squares estimators (LSEs) are proposed to estimate the coherent model parameters, and some statistical results of the LSEs are obtained, including strong consistency, strong convergence rate, and asymptotic normality. Furthermore, the LSEs-based estimators are proposed to estimate the noncoherent model parameters, and the strong consistency and the asymptotic normality are also proved. Finally, some numerical experiments are performed to see how the asymptotic results work for finite sample sizes.     

Almost Sure Convergence of Weighted Sums for Negatively Associated Random Variables

In this article, let {X₁, …, X_n} be a sequence of negatively associated random variables and {a_ni, 1 ? i ? n, n ? 1} be a triangular array of constants. Several almost sure convergence theorems for the weighted sums ∑ⁿ_{i = 1}a_niX_i are established.     

Consistency of the recursive nonparametric regression estimation for dependent functional data

We consider the recursive estimation of a regression functional where the explanatory variables take values in some functional space. We prove the almost sure convergence of such estimates for dependent functional data. Also we derive the mean quadratic error of the considered class of estimators. Our results are established with rates and asymptotic appear bounds, under strong mixing condition. Finally, the feasibility of the proposed estimator is illustrated throughout an empirical study.     

Estimating monotone convex functions via sequential shape modification

We propose a sequential method to estimate monotone convex functions that consists of: (i) monotone regression via solving a constrained least square (LS) problem and (ii) convexification of the monotone regression estimate via solving a uniform approximation problem with associated constraints. We show that this method is faster than the constrained LS method. The ratio of computation time increases as data size increases. Moreover, we show that, under an appropriate smoothness condition, the uniform convergence rate achieved by the proposed method is nearly comparable to the best achievable rate for a non-parametric estimate which ignores the shape constraint. Simulation studies show that our method is comparable to the constrained LS method in estimation error. We illustrate our method by analysing ground water level data of wells in Korea.     

我国区域规划特点、问题及区域发展新格局

我国国家级区域规划不仅成为国家战略布局和区域发展政策的重要工具,同时也应当成为引领区域经济科学发展的行动指南。制定和出台国家级区域规划必须立足于国家整体战略,能够引导区域内各主体一致行动,规划实施的关键在于建立有效的区域协调机制。未来我国沿海经济带将走向＂俱乐部趋同＂,大城市圈对区域资源配置的主导性增强,产业资本将替代劳动力成为要素流动的主体,民族地区经济的战略地位跃升。     

Statistical and computational tradeoff in genetic algorithm-based estimation

When a genetic algorithm (GA) is employed in a statistical problem, the result is affected by both variability due to sampling and the stochastic elements of algorithm. Both of these components should be controlled in order to obtain reliable results. In the present work we analyze parametric estimation problems tackled by GAs, and pursue two objectives: the first one is related to a formal variability analysis of final estimates, showing that it can be easily decomposed in the two sources of variability. In the second one we introduce a framework of GA estimation with fixed computational resources, which is a form of statistical and the computational tradeoff question, crucial in recent problems. In this situation the result should be optimal from both the statistical and computational point of view, considering the two sources of variability and the constraints on resources. Simulation studies will be presented for illustrating the proposed method and the statistical and computational tradeoff question.