Goodness of Fit for Discrete Random Variables Using the Conditional Density

In this paper we find a new test of goodness of fit in the case of discrete random variables. The main advantage of the methodology proposed in this paper relies on the fact that given the sample, we can control the probability of the type I error, that is α, and then find the exact value of the probability of the type II error, β, associated, in some cases. The results are not asymptotic, but exact. Also a conditional test for two alternatives is obtained. We also include some simulations in order to check the power of the procedures.Mathematics Subject Classification (2000) Primary 62G10 · 62B05 · Secondary 62E10     

On the Conditional Variance of Fuzzy Random Variables

The conditional variance of random variables plays an important role for well-known variance decomposition formulas. In this paper, the conditional variance is defined for fuzzy random variables and some properties are proved, which especially generalize to the mentioned variance decomposition. Moreover, results for two special types of fuzzy random variables and an outlook for possible applications are presented.     

On the Ratio of Fréchet Random Variables

The distribution of the ratio X/Y is derived when X and Y are independent Fréchet random variables. Extensive tabulations of the associated percentage points are also given.     

在最终需求为服从正态分布的随机变量时的投入产出优化控制模型

本文给出了随机投入产出决策方法的特征，讨论了在最终需求为服从正态分布的随机变量时的投入产出决策方法，并且建立了相应的随机优化控制模型。     

A Nonclassical Law of the Iterated Logarithm for Functions of Positively Associated Random Variables

Let be a sequence of stationary positively associated random variables and a sequence of positive constants be monotonically approaching infinity and be not asymptotically equivalent to loglog n. Under some suitable conditions, a nonclassical law of the iterated logarithm is investigated, i.e.

趋势成分引起的虚假回归问题解决方法研究

本文研究了由序列中趋势成分引起的虚假回归问题的解决方法。发现在模型设定式中加入趋势变量,并考虑趋势存在结构突变的情况,再根据残差是否存在自相关进行可行广义最小二乘（FGLS）或普通最小二乘（OLS）估计,可以有效解决趋势成分引起的虚假回归问题。通过理论分析表明,采用本文中的估计方法,所得检验两序列是否为虚假相关的t统计量渐近服从标准正态分布或与标准正态非常接近的分布。Monte Carlo模拟证实了该方法的有效性。最后以Yule(1926)中两高度虚假相关的时间序列为例,佐证文中结论。     

Estimation of Dynamic Nonlinear Random Effects Models with Unbalanced Panels

This paper presents estimation methods for dynamic nonlinear models with correlated random effects (CRE) when having unbalanced panels. Unbalancedness is often encountered in applied work and ignoring it in dynamic nonlinear models produces inconsistent estimates even if the unbalancedness process is completely at random. We show that selecting a balanced panel from the sample can produce efficiency losses or even inconsistent estimates of the average marginal effects. We allow the process that determines the unbalancedness structure of the data to be correlated with the permanent unobserved heterogeneity. We discuss how to address the estimation by maximizing the likelihood function for the whole sample and also propose a Minimum Distance approach, which is computationally simpler and asymptotically equivalent to the Maximum Likelihood estimation. Our Monte Carlo experiments and empirical illustration show that the issue is relevant. Our proposed solutions perform better both in terms of bias and RMSE than the approaches that ignore the unbalancedness or that balance the sample.     

双侧限计量一次抽样检验理论的应用

双侧限计量一次抽样检验的精确公式已在20世纪80年代研究成功[1],但其应用不能令人满意.本文在抽样检验精确公式的基础上,研究出精密的数值表Φ一1(α-α1)及Φ-1(β+β1),为抽样检验精确公式的进一步应用开辟了广阔的前景.文末给出了应用示例.     

基于双自适应Lasso惩罚的随机效应分位回归模型研究

研究目标：解决随机效应分位回归模型中固定效应和随机效应系数同时估计和选择问题。研究方法：对固定效应和随机效应系数同时实施自适应Lasso惩罚,并为参数估计设计交替迭代算法。研究发现：新方法不仅对随机误差分布具有较强的稳健性,而且在不同稀疏度模型下均有着良好的表现,尤其是在高维情形时。研究创新：本文提出的方法在对模型中重要自变量进行选择的同时能够充分考虑随机效应的影响;交替迭代算法不仅有效解决了需要选择两个惩罚参数的困境,而且收敛速度快。研究价值：为实际工作者对面板数据和纵向数据的分析提供了有效的建模方法。     

Assessing the Cross-Sex Validity of the Goldthorpe Class Schema Using Log-linear Models with Latent Variables

In this paper we use log-linear models with latent variables to estimate and compare the structure of job characteristics and their relation to positions in the Goldthorpe class schema for male and female employees. We proceed by estimating latent class models of job attributes and then evaluate the degree of similarity in these models between men and women. Sex differences in the patterns of association between the various latent types of job attribute and between those attributes and the Goldthorpe class schema are then assessed. The structure of job attributes is found to be generally similar for men and women; and the Goldthorpe schema is shown to be a similarly effective predictor of these attributes for both sexes. The class structure of employment relations is therefore argued to be equivalent across the sexes, so confirming the generality of class relations as conceptualised by Goldthorpe and his colleagues.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

Regression Models with Variables of Different Frequencies: The Case of a Fixed Frequency Ratio*

An increasing variety of data frequencies available in economics, finance, etc. gives rise to a question how to build and estimate a regression model with variables observed at different frequencies. In a unifying framework of (m,d)‐aggregation we consider various approaches by discussing some potential and limitations. A Monte Carlo experiment and an empirical example illustrate that the traditional fixed aggregation approach, widely used in applied economics, might be inconsistent with data and highly inferior in terms of model precision.     

Admissible and minimax estimators of 363-1363-1363-1in the gamma distribution with truncated parameter space

In this paper we consider asmissible and minimax estimation of the parameter in the gamma distribution with truncated parameter space. We give a necessary and sufficient condition for minimaxity (Theorem 1) and obtain the classes of new minimax and asmissible estimators. The results of the paper can be applied to estimation of parameters in the normal, lognormal, Pareto, generalized gamma, generalized Laplace and other distributions.     

多热源集中供热系统热源的优化调度

多热源集中供热系统热源的优化调度就是要解决多个热源如何启动,每个热源何时启动以及启动后的运行状态,调度的目的就是根据热负荷的变化情况,将热负荷合理的分配给各个热源,使得多热源集中供热系统整体供热量和燃料费用达到最小。本文采用热源和热网分步骤的调度方法,根据各个热源的最大供热量、热源的效率进行热源的调度,确定各个热源的启动时间以及不同负荷下的供热量。     

Locally minimax test of independence in elliptically symmetrical distributions with additional observations

Summary LetX=(X _ij )=(X ₁, ...,X _n )’,X’ _i =(X _i1, ...,X _ip )’,i=1,2, ...,n be a matrix having a multivariate elliptical distribution depending on a convex functionq with parameters, 0,σ. Let ϱ²=ϱ ₂ ^-2 be the squared multiple correlation coefficient between the first and the remainingp ₂+p ₃=p−1 components of eachX _i . We have considered here the problem of testingH ₀:ϱ²=0 against the alternativesH ₁:ϱ ₁ ^-2 =0, ϱ ₂ ^-2 >0 on the basis ofX andn ₁ additional observationsY ₁ (n ₁×1) on the first component,n ₂ observationsY ₂(n ₂×p ₂) on the followingp ₂ components andn ₃ additional observationsY ₃(n ₃×p ₃) on the lastp ₃ components and we have derived here the locally minimax test ofH ₀ againstH ₁ when ϱ ₂ ^-2 →0 for a givenq. This test, in general, depends on the choice ofq of the familyQ of elliptically symmetrical distributions and it is not optimality robust forQ.     

A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors

Abstract

This paper proposes a new generalized method of moments (GMM) estimator for spatial panel models with spatial moving average errors combined with a spatially autoregressive dependent variable. Monte Carlo results are given suggesting that the GMM estimator is consistent. The estimator is applied to English real estate price data.     

时空动态半参数变系数随机效应面板模型的估计

研究目标：克服半参数变系数面板模型中扰动项和因变量存在时空动态性问题。研究方法：提出一类更加一般化的时空动态半参数变系数随机效应面板模型,并构建截面似然估计量。研究发现：估计量具有良好的小样本性质,估计误差随着样本总量的提高而减小,在Case空间矩阵下,空间滞后和时空滞后系数的估计精度随空间复杂度的增大而降低,用该方法分析我国外商直接投资、知识产权保护与经济增长关系,进一步证实了模型的适用性。研究创新：证明了估计量满足一致性和渐近正态性,数值模拟考察了估计量的小样本性质。研究价值：拓展了现有半参数变系数空间面板模型的形式,增强了模型的适用性和解释力,有益于经济问题实证研究的开展。     

Methods for the Analysis of Explanatory Linear Regression Models with Missing Data Not at Random

Since the work of Little and Rubin (1987) not substantial advances in the analysisof explanatory regression models for incomplete data with missing not at randomhave been achieved, mainly due to the difficulty of verifying the randomness ofthe unknown data. In practice, the analysis of nonrandom missing data is donewith techniques designed for datasets with random or completely random missingdata, as complete case analysis, mean imputation, regression imputation, maximumlikelihood or multiple imputation. However, the data conditions required to minimizethe bias derived from an incorrect analysis have not been fully determined. In thepresent work, several Monte Carlo simulations have been carried out to establishthe best strategy of analysis for random missing data applicable in datasets withnonrandom missing data. The factors involved in simulations are sample size,percentage of missing data, predictive power of the imputation model and existenceof interaction between predictors. The results show that the smallest bias is obtainedwith maximum likelihood and multiple imputation techniques, although with lowpercentages of missing data, absence of interaction and high predictive power ofthe imputation model (frequent data structures in research on child and adolescentpsychopathology) acceptable results are obtained with the simplest regression imputation.     

Increasing failure rate and decreasing reversed hazard rate properties of the minimum and maximum of multivariate distributions with log-concave densities

For a multivariate random vector X = (X ₁,...,X _n ) with a log-concave density function, it is shown that the minimum min{X ₁,...,X _n } has an increasing failure rate, and the maximum max{X ₁,...,X _n } has a decreasing reversed hazard rate. As an immediate consequence, the result of Gupta and Gupta (in Metrika 53:39–49, 2001) on the multivariate normal distribution is obtained. One error in Gupta and Gupta method is also pointed out.      

开放多相系熵密度平衡方程在暴雨预报中的应用

利用耗散结构理论的相关知识,从非平衡态理论的熵密度平衡方程出发,计算了2004年6月23日发生在湖南西部和北部地区暴雨发生前24 h至暴雨结束前12 h每隔6 h对流层中低层的熵变场.通过对各个不同时刻的熵变场的分析与比较,讨论了熵场变化与暴雨区域、强度及其演变之间的关系,为暴雨强度和落区预报提供了参考指标.