Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model

In the presence of multicollinearity in logistic regression, the variance of the Maximum Likelihood Estimator (MLE) becomes inflated. Siray et al. (2015) [1] proposed a restricted Liu estimator in logistic regression model with exact linear restrictions. However, there are some situations, where the linear restrictions are stochastic. In this paper, we propose a Stochastic Restricted Maximum Likelihood Estimator (SRMLE) for the logistic regression model with stochastic linear restrictions to overcome this issue. Moreover, a Monte Carlo simulation is conducted for comparing the performances of the MLE, Restricted Maximum Likelihood Estimator (RMLE), Ridge Type Logistic Estimator(LRE), Liu Type Logistic Estimator(LLE), and SRMLE for the logistic regression model by using Scalar Mean Squared Error (SMSE).

Improvement of the Preliminary Test Estimator When Stochastic Restrictions are Available in Linear Regression Model

Ridge type estimators are used to estimate regression parameters in a multiple linear regression model when multicolinearity exists among predictor variables. When different estimators are available, preliminary test estimation procedure is adopted to select a suitable estimator. In this paper, two ridge estimators, the Stochastic Restricted Liu Estimator and Liu Estimator are combined to define a new preliminary test estimator, namely the Preliminary Test Stochastic Restricted Liu Estimator (PTSRLE). The stochastic properties of the proposed estimator are derived, and the performance of PTSRLE is compared with SRLE in the sense of mean square error matrix (MSEM) and scalar mean square error (SMSE) for the two cases in which the stochastic restrictions are correct and not correct. Moreover the SMSE of PTSRLE based on Wald (WA), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests are derived, and the performance of PTSRLE is compared using WA, LR and LM tests as a function of the shrinkage parameter d with respect to the SMSE. Finally a numerical example is given to illustrate some of the theoretical findings.

Parameter estimation of the stochastic AMR model and its application to the study of several strong earthquakes

Based on the stochastic AMR model, this paper constructs man-made earthquake catalogues to investigate the property of parameter estimation of the model. Then the stochastic AMR model is applied to the study of several strong earthquakes in China and New Zealand. Akaikes AIC criterion is used to discriminate whether an accelerating mode of earthquake activity precedes those events or not. Finally, regional accelerating seismic activity and possible prediction approach for future strong earthquakes are discussed.

Parameter estimation of the stochastic AMR model and its application to the study of several strong earthquakes

Based on the stochastic AMR model, this paper constructs man-made earthquake catalogues to investigate the property of parameter estimation of the model. Then the stochastic AMR model is applied to the study of several strong earthquakes in China and New Zealand. Akaikes AIC criterion is used to discriminate whether an accelerating mode of earthquake activity precedes those events or not. Finally, regional accelerating seismic activity and possible prediction approach for future strong earthquakes are discussed.

PARAMETER ESTIMATION OF PATH-DEPENDENT MCKEAN-VLASOV STOCHASTIC DIFFERENTIAL EQUATIONS

This work concerns a class of path-dependent McKean-Vlasov stochastic differential equations with unknown parameters.First,we prove the existence and uniqueness of these equations under non-Lipschitz conditions.Second,we construct maximum likelihood estimators of these parameters and then discuss their strong consistency.Third,a numerical simulation method for the class of path-dependent McKean-Vlasov stochastic differential equations is offered.Finally,we estimate the errors between solutions of these equations and that of their numerical equations.

我国上市公司投资非效率的动态识别与预警——基于Two-tier Stochastic frontier的视角

本文运用双边随机前沿模型取代Richardson(2006)模型,运用MLE估计优化OLS估计,提出了双边随机前沿模型更有利于解决人们应对非效率投资问题的设想,构建了对公司非效率投资展开高效分析和合理建议的新型工具。结果表明:双边随机前沿模型的MLE估计能胜任对非效率投资的快捷识别及预警工作,同时发现现行我国上市公司投资整体状况为投资不足,极端非效率投资现象并不多见,轻度非效率投资的企业需要关注拉升投资行为的不良后果,重度非效率投资的企业需要谨防压低投资的负面效应,自由现金流有加大过度投资的倾向,负债率有诱发投资不足的风险。

Estimation and Testing for the Parameters of AR(p)-ARCH(q) under Ordered Restriction

Suppose that the time series Xt satisfieswhere α0≥δ＞0,αi≥0 for i=1,2,…,q;βi,i=1,…,p, are real numbers; p and q are the order of the model. The sequence {ξt};（0,1） and is independent of {hs,s≤t} for fixed t. The above model is usually written as AR（p）-ARCH（q）.We consider stationary series AR（p）-ARCH（q） model and assume the stationary field is θ0. We express this statement asH1:α1≥α2…≥αq,β1≥β2≥…≥βp and we consider an order restricted testing problem, which is to testH0:α1=α2=…=αq,β1=β2=…=βpagainst H1-H0. We derive the likelihood ratio （LR） test statistic and its asymptotic distri-

Estimation and Testing for the Parameters of AR(p)-ARCH(q) under Ordered Restriction

In this paper, we study a stationary AR（p）-ARCH（q） model with parameter vectors α and β. We propose a method for computing the maximum likelihood estimator （MLE） of parameters under the nonnegative restriction. A similar method is also proposed for the case that the parameters are restricted by a simple order： α1≥α2≥…≥αq and β1≥β2≥…≥βp. The strong consistency of the above two estimators is discussed. Furthermore, we consider the problem of testing homogeneity of parameters against the simple order restriction. We give the likelihood ratio （LR） test statistic for the testing problem and derive its asymptotic null distribution.

Interval Estimation in a Two Parameter Weibull Distribution Based on Type-2 Censored Data

In this paper, we consider the construction of the approximate profile- likelihood confidence intervals for parameters of the 2-parameter Weibull distribution based on small type-2 censored samples. In previous research works, the traditional Wald method has been used to construct approximate confidence intervals for the 2-parameter Weibull distribution under type-2 censoring scheme. However, the Wald technique is based on normality assumption and thus may not produce accurate interval estimates for small samples. The profile-likelihood and Wald confidence intervals are constructed for the shape and scale parameters of the 2-parameter Weibull distribution based on simulated and real type-2 censored data, and are hence compared using confidence length and coverage probability.

Maximum Likelihood Estimation of Ratios of Means and Standard Deviations from Normal Populations with Different Sample Numbers under Semi-Order Restriction

For two normal populations with unknown means μi and variances σi2 > 0, i = 1,2, assume that there is a semi-order restriction between ratios of means and standard deviations and sample numbers of two normal populations are different. A procedure of obtaining the maximum likelihood estimators of μi’s and σi’s under the semi-order restrictions is proposed. For i = 3 case, some connected results and simulations are given.

Estimation in Interacting Diffusions: Continuous and Discrete Sampling

Consistency and asymptotic normality of the sieve estimator and an approximate maximum likelihood estimator of the drift coefficient of an interacting particles of diffusions are studied. For the sieve estimator, observations are taken on a fixed time interval [0,T] and asymptotics are studied as the number of interacting particles increases with the dimension of the sieve. For the approximate maximum likelihood estimator, discrete observations are taken in a time interval [0,T] and asymptotics are studied as the number of interacting particles increases with the number of observation time points.

Quadratic Loss of Isotonic Normal Means under Simultaneous Order Restrictions

For two normal populations with unknown means μ and unknown variances σ2, assume that there are simple order restrictions among the means and variances: μ1 < μ2 and σ12 >σ22 > 0. This case is said to be simultaneous order restriction by Shi (Maximum likelihood estimation of means and variances from normal populations under simultaneous order restrictions, J. Multivariate Anal., 50(1994), 282-293.) and an iterative algorithm of computing the order restricted maximum likelihood estimates of μi and σi2 was given in that paper. This paper shows that the restricted maximum likelihood estimate of μi has smaller mean square loss than the usual estimate xi under some conditions.

Estimation of Ordered Means of Two Normal Distributions with Ordered Variances

The authors consider the problem of estimating the ordered means of two normal distributions with unknown ordered variances. The authors discuss the estimation of two ordered means, individually, in terms of stochastic domination and MSE （mean squared error）. The authors show that in estimating the mean with larger variance, the usual estimator under order restriction on means can be improved upon. However, in estimating the mean with smaller variance, the usual estimator can＇t be improved upon even under MSE. The authors also discuss simultaneous estimation problem of two ordered means when unknown variances are ordered.

顾及RMLE的GNSS时序噪声特性及环境负载修正

针对环境负载易对全球卫星导航系统(GNSS)站坐标时序修正前后噪声模型特性及其站速度造成影响的问题,提出一种顾及约束最大似然估计(RMLE)的GNSS时序噪声特性及环境负载修正方法:选用206个连续13a的GNSS站时间序列,采用改进的贝叶斯信息模型估计准则,利用约束最大似然估计(RMLE)方法探讨和分析大气压负载、非海洋潮汐负荷、积雪负载及土壤水负载等环境负载对GNSS时序修正前后噪声模型特性及其站速度的影响。结果表明:经环境负载修正后,GNSS时序中的噪声主要表现为闪烁噪声+白噪声(FNWN)与幂律噪声+白噪声(PLWN),北(N)、东(E)、天(U)3个分量上噪声模型修正前后分别有约19.9%、36.5%、40.8%的噪声模型发生变化,N、E、U分量上分别有96.6%、84.5%、88.3%的测站速度不确定度减小,对垂直速度估计的最大影响可达0.5mm/a;证明环境负载修正能够提高速度估值的精度。

功能作图的混合效应模型开发

【目的】以大肠杆菌菌株丰度和功能作图模型为研究基础,通过在功能作图模型中引入群体的固定效应和个体间亲缘关系造成的随机效应,探究混合效应对功能作图模型性能的影响。【方法】本研究在功能定位框架基础上,使用大肠杆菌动态培养的生长数据作为实际案例,将亚群和SNP基因型作为固定效应的来源,将固定效应因素融入定位模型,提出了Q矩阵模型的扩展;在保留使用方差–协方差模型对随机残差建模的前提下,使用Legendre模型对随机效应建模,开展了固定效应加一般性的随机效应的混合效应模型分析(模型1);利用限制性似然估计的方法推导其方差–协方差参数、随机效应和固定效应,开展了固定效应和亲缘关系造成随机效应的混合模型(模型2)分析;利用Zwald检验法推导各标记位点p值的计算方法。【结果】(1)2种模型中,95%的标记呈现出p值与期望值相吻合的特点,p值分布在QQ图中的上翘结果满意。(2)模型2相比模型1检测到了更多的SNP位点,表明模型2对亲缘关系造成的随机效应解释性更强。(3)计算机模拟结果显示:样本量较小、遗传力较低时,模型假阳性率为4.77%;当样本量为800,遗传力为1%时,模型对QTL的发现率可超过70%;或当样本量为400,遗传力超过1.5%时,QTL发现率也可超过70%。【结论】本研究提出的在功能作图模型中引入固定效应和亲缘关系造成的随机效应的混合模型的方法较好地完善了功能定位理论,对固定效应中的协变量因素具有较好的校正功能,可有效剖分随机效应与剩余残差;为后续完善功能定位,开发固定效应加亲缘关系(Q+K)模型的软件包奠定了良好基础。

加权最大似然波达方向估计算法及其应用研究

针对阵列信号处理中空间相干性低、频域有色且通道间具有相同功率谱的噪声模型,分别基于确定性信号模型与随机性信号模型,提出一种加权最大似然(Weighted Maximum Likelihood,WML)波达方向估计算法。数据仿真实验表明,该算法提高了由空间非相干且一致有色噪声引起的低信噪比条件下的波达方向估计精度。户外实验验证了该算法在风噪声条件下的有效性。

考虑测量误差的步进加速退化试验建模与剩余寿命估计

步进应力加速退化试验已发展成高可靠、长寿命产品可靠性评估与剩余寿命估计的主要试验方法。为研究测量误差在基于步进应力加速退化试验方法剩余寿命估计中的影响,建立了基于Wiener过程的考虑个体差异和测量误差的退化模型。将Wiener过程的漂移系数随机化描述个体差异,在首达时间意义下得到了寿命分布的概率密度函数。基于极大似然估计法对模型中引入的未知参数进行估计。采用蒙特卡洛方法对激光器的性能退化进行了仿真研究。研究结果表明,考虑测量误差的退化模型的模型拟合性和剩余寿命估计精度都优于不考虑测量误差的方法,可以提高可靠性估计的精度与剩余寿命预测的准确性。

随机生产前沿方法的发展及其在中国的应用

本文对随机前沿生产函数模型的发展及其在中国生产率分析中的应用进行了评述。文章首先介绍随机前沿方法的基本原理、估计方法和在面板数据下对全要素生产率增长的分解,随后评述随机前沿生产函数模型的最新进展和在经验分析中的优势与作用,最后总结了在中国行业和地区经济增长研究中随机前沿方法的成果和不足,并探讨今后研究的发展方向。

中国股票市场的时变杠杆效应研究——基于随机Copula模型的实证分析

构建随机Copula模型研究了中国股票市场在极端市场条件下的时变杠杆效应.为了解决金融市场中波动率不可直接观测的问题,采用已实现波动率测度作为隐波动率的代理变量,进而运用基于有效重要性抽样的极大似然(EIS-ML)方法估计了随机Copula模型的参数.基于沪深股市数据的实证研究表明:中国股票市场的杠杆效应具有非对称特征,即股市低收益率伴随高波动率,但股市高收益率不一定伴随低波动率;中国股票市场的杠杆效应存在显著的时变性,沪深股市杠杆效应表现出类似的变化趋势;随机Copula模型相比其它Copula模型(静态Copula模型和时变Copula模型)具有更好的数据拟合效果.

基于随机波动模型的短期负荷预测

研究了负荷时间序列波动性,考虑方差时变特征,提出了基于随机波动(SV)模型的短期负荷预测方法。引入伪极大似然估计解决SV参数估计问题,进而将模型转换为状态空间方程,利用卡尔曼滤波获取标准SV模型参数。另外,还将模型推广为非高斯假设SV模型。利用动态波动曲线的构建,讨论了负荷时间序列条件方差的时变性特征。基于日用电量数据建立了SV族日负荷预测模型,并利用平均绝对百分误差、均方误差、TIC 3种指标将SV族模型预测结果与广义自回归条件异方差(GARCH)模型做了比较,得到SV族模型的前2种指标均小于GARCH模型,而且SV模型的TIC指标更接近于零。算例分析表明了SV族负荷预测模型的可行性和有效性。