随机信息中正态方差的灰色无偏估计研究

文章在Neyman的置信区间理论基础上,借助灰色系统的方法,在随机样本的信息下,对正态方差的灰色有偏估计和灰色无偏估计进行了研究,求出了正态方差的灰数无偏估计及其白化权函数,并举例以示其应用。     

随机信息中正态方差的灰色估计

利用随机信息进行参数估计,是数理统计学的基本内容.但经典统计学的理论和方法,都是建立在参数是明确数据的基础上.而现实社会经济生活中的参数,具有大量不确定性或认识的模糊灰色性.文章在Neyman的置信区间理论基础上,借助灰色系统的方法,在随机样本的信息下,对正态方差的灰色估计进行了研究,求出了正态方差的灰数估计及其白化权函数;并列举实例以示其应用.     

正态总体方差的一种间接预估方法

鉴于极差比方差更容易获得,所以利用极差对正态总体方差进行间接预估以确定样本量的想法很有实用价值.本文利用随机模拟方法和线性回归分析得到dn的一个简明表达式dn=0.51n(n)+3,从而由此间接获得一个正态总体方差的估计值σ2=[Rn/(0.51n(n)+3)]2,这将使直接利用"更便宜的"极差确定样本量具有可操作性.     

基于两正态均值的灰色统计假设检验研究

两个正态总体均值差的区间估计和假设检验研究是数理统计学的基本内容,但经典统计学的两个正态总体均值区间估计和假设检验理论,是建立在确定的随机数据上的区间估计和假设检验.而现实社会生活中很多数据具有模糊灰色等不确定性,面对这类不确定性数据,如何较为合理地进行科学分析和判断.在灰色系统理论的基础上,文章建立了在随机样本信息下,两个正态均值的灰色区间估计和灰色假设检验方法,从而把随机信息的两正态均值假设检验理论拓展到灰色数据信息中,并把这一灰色检验方法应用于医学统计实例分析.     

随机样本中正态均值的灰色区间估计研究

利用随机样本信息对参数估计是数理统计学的基本内容,但面对模糊或灰色数据如何更好地进行参数估计.在Neyman的正态分布均值的置信区间理论基础上,借助于灰色系统分析的方法,研究了在随机信息下的灰色区间估计问题,能比点估计或Neyman的置信区间更好地提供有效信息.并进行实例应用.     

基于对数正态分布联合对数均值与对数方差模型的极大似然估计

文章基于对数正态分布研究提出了联合对数均值与对数方差模型,给出了此模型参数的极大似然估计,模拟和实例研究结果表明该模型和方法是有用和有效的。     

一种异方差模型的两阶段估计

在异方差线性回归模型中,当模型误差项的协方差阵未知时,对异方差模型进行估计目前还没有比较好的方法。基于此,提出一种异方差模型的两阶段估计—基于异方差一致协方差阵估计,该方法将异方差一致协方差阵估计HC5m和广义最小二乘估计法结合起来,综合使用全部样本的信息,并对异方差模型进行估计。通过大量的蒙特卡洛数值模拟和实证分析,结果表明该方法具有一定的可行性和有效性。     

两个正态总体均值之差的区间估计解析

文章对两个正态总体均值之差的区间估计进行教学上的解析,尤其针对非均衡样本下方差未知且不相等情形,利用例举时比法说明应如何选择合适的枢轴量.     

正态方差混合分布新息GARCH模型的EM估计

近来,人们对实际数据使用厚尾分布进行建模颇感兴趣。一种流行的考虑就是所谓的广义自回归条件异方差(GARCH)模型。不幸的是,在一些应用中正态新息的GARCH模型的尾部不够厚。文章提出新息为正态方差混合分布的GARCH模型并给出了使用EM算法对模型参数作估计的步骤。结果表明,新息为正态方差混合新息分布的GARCH模型比正态新息的GARCH模型有更厚的尾部,因而更能捕捉实际数据中的厚尾特征。文章还以上证指数为例阐述了这一结论。     

非对称单位根检验和非参数方差比

基于三种退势方法较详细研究了方差比检验在非对称单位根检验中的适用性,并通过MC模拟揭示了其检验势性质。结果表明：在不含趋势项的TAR下,两机制TAR数据落在第一机制的比率是影响方差比检验势的重要因素,且比率越高检验势也越高;三机制TAR中落在中间机制的数据比率会影响检验势,随着比率增加检验势呈下降趋势,但程度不大。在含趋势的TAR下,由于趋势项在数据生成过程中具有支配作用,各种检验势会随着趋势设定的不同而不同。数据在不同机制之间的转换概率越高,则ROLS和RDM退势较OLS退势具有明显优势。     

Variance Estimation Based on Invariance Principles

Consistent variance estimators for certain stochastic processes are suggested using the fact that (weak or strong) invariance principles may be available. Convergence rates are also derived, the latter being essentially determined by the approximation rates in the corresponding invariance principles. As an application, a change point test in a simple AMOC renewal model is briefly discussed, where variance estimators possessing good enough convergence rates are required.     

分层回归估计与分层比估计的方差性质

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On the Ratio of Two Folded Normal Distributions

A class of distributions associated with the ratio of two folded normal random variables is introduced which strictly includes the half standard Cauchy distribution. The properties of this class of distributions are studied, along with a graph of the possible shapes of its density functions. The salient features of this class are mathematical tractability and statistical applicability. Utility of the class of distributions is demonstrated by presenting four applications.     

Density of the Ratio of Two Normal Random Variables and Applications

In reply to a question raised in the literature, and to settle an argument debated in the last decades, we give the exact closed form expression of the density of X/Y, where X and Y are normal random variables, in terms of Hermite and confluent hypergeometric functions. All cases will be considered: standardized and nonstandardized variables, independent or correlated variables. Examples in applied disciplines are presented, and generalizations to ratios of variables from scale mixtures of bivariate normal distributions show the potential of further new applications in applied statistics and operations research.     

Estimation in a Two Variance Components Model When one Component is Known

We study a mixed linear model with two variance components. We suppose that one component is known. The objective of the paper is the estimation of the unknown component. The usual MINQE estimators seem to be unadapted to the problem. So we propose a new family of quadratic estimators, based on a natural class of estimators and the idea upon which the MINQE theory is built. All the estimators are compared on simulated data.     

The Exact Likelihood Ratio Test for Equality of Two Normal Populations

Testing the equality of two independent normal populations is a perfect case of the two-sample problem, yet it is not treated in the main text of any textbook or handbook. In this article, we derive the exact distribution of the likelihood ratio test and implement this test with an R function. This article has supplementary materials online.     

Variance Estimation in Spatial Regression Using a Non-parametric Semivariogram Based on Residuals

Abstract.  The empirical semivariogram of residuals from a regression model with stationary errors may be used to estimate the covariance structure of the underlying process. For prediction (kriging) the bias of the semivariogram estimate induced by using residuals instead of errors has only a minor effect because the bias is small for small lags. However, for estimating the variance of estimated regression coefficients and of predictions, the bias due to using residuals can be quite substantial. Thus we propose a method for reducing this bias. The adjusted empirical semivariogram is then isotonized and made conditionally negative-definite and used to estimate the variance of estimated regression coefficients in a general estimating equations setup. Simulation results for least squares and robust regression show that the proposed method works well in linear models with stationary correlated errors.     

Inference on the Common Variance of Correlated Normal Random Variables

ABSTRACT

Scale equivariant estimators of the common variance σ², of correlated normal random variables, have mean squared errors (MSE) which depend on the unknown correlations. For this reason, a scale equivariant estimator of σ² which uniformly minimizes the MSE does not exist. For the equi-correlated case, we have developed three equivariant estimators of σ²: a Bayesian estimator for invariant prior as well as two non-Bayesian estimators. We then generalized these three estimators for the case of several variables with multiple unknown correlations. In addition, we developed a system of confidence intervals which produce the desired coverage probability while being efficient in terms of expected length.     

Estimation of Ratio of Two Finite-Population Means in the Presence of Non Response

It is well known that a ranked set sample under perfect ranking provides more information than an i.i.d. sample of the same size. Then it may be interesting to study how much information is lost due to imperfect ranking. In this article, we consider some ranking mechanisms and study the loss of the Fisher information according to the degree of imperfect ranking. Then we continue to discuss the optimal combination of the sample size and number of strata in terms of maximizing the Fisher information for the bivariate normal and exponential distributions.     

Interval Estimation for the Sortino Ratio and the Omega Ratio

In this article, asymptotic confidence intervals (CIs) for the Sortino and Omega ratios are proposed and analyzed. First, the CIs are derived under the assumption of temporal independence and identical distribution of returns. Later they are obtained assuming that the returns process is strictly stationary and α-mixing of a certain size. In order to evaluate the minimum sample size for a good coverage accuracy of the asymptotic CIs, a simulation study is performed. It is obtained that the minimum sample sizes are very high, especially under the more realistic assumption of not-iid returns.