空间自相关地理加权回归模型的估计

地理加权回归作为一类能有效处理回归分析中空间非平稳性现象的建模技术,在多类问题的研究得到了广泛的应用.主要讨论这类空间计量经济学模型在空间自相关情形下的估计问题.首先,对于因变量含有空间滞后项的地理加权回归模型,分别给出了局部似然估计和两步估计两种方法.其次,考虑了误差空间自相关下地理加权回归模型的估计问题.     

混合地理加权空间自回归模型的Profile GMM估计

为了更好地同时考虑空间自相关性和空间异质性,本文研究一类空间自回归混合地理加权回归模型.基于Profile方法和广义矩(GMM)方法,构造了模型中未知空间自回归参数,常数回归系数和系数函数的两类Profile GMM估计.数值模拟结果表明所提出的估计在有限样本中表现良好.     

基于地理加权回归模型的塔里木河流域农业气候资源研究

以塔里木河流域农业气候资源为研究对象,通过收集该流域38个县市地区1995-2014年的气象、地理及经济方面的指标数据,运用R软件对其进行因子分析,得出年平均气温对该流域种植业具有明显的影响.然后,建立了地理加权回归模型,对收集的指标数据进行分析,研究该流域农业气候资源空间聚集与差异.结果表明:农业气候资源对塔河流域农业生产存在明显的空间影响.最后,为流域农作物的合理种植给出建议.     

回归模型误差相关性的统一检验方法

提出了回归模型误差项关于实对称矩阵相关的概念，从而将探测误差项的各种相关性，诸如序列自相关、空间自相关以及趋势性等问题纳入统一的统计检验框架内．在线性回归模型下，提出了一种计算检验p-值的三阶矩χ^2逼近方法．与精确方法相比，该逼近方法不但显著地降低了计算量，而且模拟计算表明具有相当高的精度．     

基于混合地理加权回归模型的小域估计

近年来,基于空间模型的小域估计方法在抽样调查领域得到越来越多的关注.提出了一类混合地理加权单元层次小域模型用以刻画空间非平稳性,其中回归系数一部分为常数,另一部分则是随着地理位置的变化而变化.基于模型给出了域均值的估计,并研究了该估计量的均方误差及其估计.     

一种新的空间计量模型——部分线性可加自回归模型及其应用

本文提出一种新的空间计量模型,部分线性可加空间自回归模型。首先,引入样条逼近可加函数分量,然后利用截面拟似然的思想得到模型参数的估计;所提出的估计方法简单易行,且对误差分布的要求低。通过模拟研究,得到所提出估计方法的有限样本性质,模拟结果显示了所提出估计的有效性;最后,将估计方法应用到波士顿房屋数据进行统计分析,得到了较他人估计更好的结果。     

基于多尺度地理加权回归的黄河流域空气质量影响因素空间效应解析

以2017年黄河流域城市空气质量监测数据为研究对象,首先给出了空气质量的时空变化特征,然后利用多尺度地理加权回归(MGWR)模型分析各因素在不同尺度下对空气质量作用的空间异质性。结果显示:(1) 2017年黄河流域中下游大部分地区的空气质量指数(AQI)处于轻度污染水平,季度的AQI呈冬高夏低的特征,其中,冬季处于中度污染水平的地区主要集中在黄河中下游地区。月度的AQI呈“U”型分布,其拐点出现在8月份;(2)第二产业比重、城镇人口比例、总人口数、降雨量和温度对空气质量的影响显著,其中,第二产业比重和城镇人口比例全局影响空气质量,总人口数在中等异质水平下影响空气质量,降雨量和温度在较高异质水平下影响空气质量;(3)第二产业比重、城镇人口比例和总人口数对空气质量的影响为正向,而降雨量和温度对空气质量的影响为负向,其中,总人口数对黄河中游地区影响较大,降雨量对黄河上游地区影响较大,温度对黄河中下游地区影响较大。     

我国城镇居民消费与收入关系的空间自回归模型研究

本文利用我国31个省市2005年的城镇居民消费和收入统计数据,对我国城镇居民的消费与收入关系进行了空间自回归模型研究。研究结果表明,我国城镇居民的消费存在显著的空间相关性.在此基础上,对所建立的空间自回归模型进行了比较、选择和评价.     

半参数空间分位回归模型的估计与变量选择

对于高维空间数据,利用半参数空间自回归进行建模,模型中会同时存在内生性、非线性、变量过多等问题。本文研究半参数空间分位回归模型,提出了新的估计程序:首先利用样条基函数,对模型中未知平滑函数进行逼近,解决非线性问题;然后运用特征向量空间滤波,将空间滞后因子转化为空间代理变量的线性组合,有效解决了内生性问题;利用再中心化影响函数,进行无条件分位回归建模,能够刻画不同分位水平下变量之间的关系;最后引入自适应Lasso惩罚,对高维线性部分进行变量选择,得到系数的稀疏估计,有效增强了模型的可解释性。数值模拟中对参数作不同的设置,展现了本文提出方法的有效性。最后,利用半参数空间分位回归模型分析了住房销售价格数据集。     

基于空间自回归性的低保标准模型的建立

为实现城乡低保标准的调整工作更加规范,需要参照扶贫标准和全国平均低保标准进行论证和科学的测算,现有低保标准对居民经济社会发展水平和财政承受能力了解的还不够全面,因此建立一种“基于空间自回归性低保标准”的数学模型来解决以上问题.通过统计分析方法对其进行推理假设,确定最优计算指标,进行针对性查找,将主要指标划分为8个影响因素,发现相邻两个地区的低保应由本地区的经济发展状况和相邻地区对本地区的经济影响两个因素决定.通过回归评价指标RMSE及MAE对模型性能进行评价,归一化处理后的空间自回归模型RMSE为0.0477MAE为0.1625,在两个指标上同时具有较好的预测性能及鲁棒性.     

The effect of information in separable Bayesian semi-Markov control models and its application to investment planning

We consider a separable Bayesian semi-Markov control model to describe economic decisions under uncertainty. Our main interest is to examine the influence of the possibility of learning on the economic decisions and on the total expected return in a multi-period framework. We make use of the concept of Blackwell-sufficiency and apply the results to multi-period investment planing under uncertainty.     

Single machine batch scheduling to minimize the weighted number of late jobs

The single machine batch scheduling problem to minimize the weighted number of late jobs is studied. In this problem,n jobs have to be processed on a single machine. Each job has a processing time, a due date and a weight. Jobs may be combined to form batches containing contiguously scheduled jobs. For each batch, a constant set-up time is needed before the first job of this batch is processed. The completion time of each job in the batch coincides with the completion time of the last job in this batch. A job is late if it is completed after its due date. A schedule specifies the sequence of jobs and the size of each batch, i.e. the number of jobs it contains. The objective is to find a schedule which minimizes the weighted number of late jobs. This problem isNP-hard even if all due dates are equal. For the general case, we present a dynamic programming algorithm which solves the problem with equal weights inO(n ³) time. We formulate a certain scaled problem and show that our dynamic programming algorithm applied to this scaled problem provides a fully polynomial approximation scheme for the original problem. Each algorithm of this scheme has a time requirement ofO(n ³/ +n ³ logn). A side result is anO(n logn) algorithm for the problem of minimizing the maximum weight of late jobs.Supported by INTAS Project 93-257.     

Method of weighted residuals as applied to nonlinear differential equations

A new method for solving a class of nonlinear boundary-value problems is presented. In this method, the nonlinear equation is linearized by guessing an initial solution and using it to evaluate the nonlinear terms. Next, a method of weighted residuals is applied to transform the linearized form of the boundary value problem to an initial value problem. The second (improved) solution is obtained by integrating the initial value problem by a fourth order Runge-Kutta scheme. The entire process is repeated until a desired convergence criterion is achieved.     

Development and application of multiscale models of acute viral infections in intervention research

The objective of this study is to advance two frontiers in multiscale modelling of acute viral infections, which are (a) the mathematical technology or technical frontier, where we present a new method for development of multiscale models of acute viral infections using influenza A virus (IAV) as a paradigm in which a new set of metrics to measure both individual level and community level infectiousness are introduced, and (b) the scientific applications frontier, where we demonstrate the implementation of multiscale modelling in evaluating the comparative effectiveness of IAV health interventions from efficacy data. The multiscale model is developed by integrating the within-host scale and the between-host scale. Using the example of IAV as a paradigm, we demonstrate the utility and process by which multiscale modelling can be used to evaluate the comparative effectiveness of health interventions that operate at different scale domains. The multiscale modelling is general enough to be applicable to other acute viral infections.     

隐非齐次马尔可夫模型的强大数定律

在状态集都有限的情况下,给出了隐马尔可夫模型的一些性质定理．利用马氏链的强极限定理,得到了隐非齐次马尔可夫模型的强大数定律．     

On the asymptotic behavior of the parameter estimators for some diffusion processes: application to neuronal models

We consider a sample of i.i.d. times and we interpret each item as the first-passage time (FPT) of a diffusion process through a constant boundary. The problem is to estimate the parameters characterizing the underlying diffusion process through the experimentally observable FPT’s. Recently in Ditlevsen and Lánsky (Phys Rev E 71, 2005) and Ditlevsen and Lánsky (Phys Rev E 73, 2006) closed form estimators have been proposed for neurobiological applications. Here we study the asymptotic properties (consistency and asymptotic normality) of the class of moment type estimators for parameters of diffusion processes like those in Ditlevsen and Lánsky (Phys Rev E 71, 2005) and Ditlevsen and Lánsky (Phys Rev E 73, 2006). Furthermore, to make our results useful for application instances we establish upper bounds for the rate of convergence of the empirical distribution of each estimator to the normal density. Applications are also considered by means of simulated experiments in a neurobiological context.      

Markov chain Monte Carlo estimation of autoregressive models with application to metal pollutant concentration in sludge

This paper provides an introduction to and an overview of Bayesian estimation, based on Markov chain Monte Carlo techniques. Autoregressive time series models are considered in some detail. Finally, an example involving the modelling of a metal pollutant concentration in sludge is presented.     

Weighted estimation of single index models with right censored responses

In this paper, the unknown link function, the direction parameter, and the heteroscedastic variance in single index models are estimated by the random weight method under the random censorship, respectively. The central limit theory and the convergence rate of the law of the iterated logarithm for the estimator of the direction parameter are derived, respectively. The optimal convergence rates for the estimators of the link function and the heteroscedastic variance are obtained. Simulation results support t...     

The integrality of the lot size in the basic EOQ and EPQ models: Applications to other production-inventory models

In this paper we present a method to obtain the solution of the classic economic order quantity (EOQ) and economic production quantity (EPQ) models when the lot size must be an integer quantity. This approach is operatively very simple and allows obtaining a rule to discriminate between the situation in which the optimal solution is unique and when there are two optimal solutions. Also, this method is applicable to the resolution of other production-inventory models. We expose some of them and illustrate the use of the method with numerical examples.