Asymptotic Normality of Cross-correlogram Estimates of the Response Function

The problem of estimation of an unknown response function of a time-invariant continuous linear system is considered. Discrete-time sample input–output cross-correlograms are taken as estimates of the response function. The inputs are supposed to be zero-mean stationary Gaussian processes close, in some sense, to a white noise. Both asymptotic normality of finite-dimensional distributions of the estimates and their asymptotic normality in spaces of continuous functions are studied. Our basic tool is a new integral representation for cumulants of the estimate as a finite sum of integrals involving cyclic products of kernels. Some inequalities for these integrals are obtained and their asymptotic behaviour is studied.     

门限自回归模型参数最小二乘估计的强收敛速度

本文在给定门限自回归模型阶数、门限和延迟参数的情况下，证明了一般门限自回归模型参数和残差方差的最小二乘估计的强收敛速度为Ｏ（（ｌｏｇｌ９ｏｇｎ／ｎ）１／２），并证明了残差方差的最小二乘估计具有渐近正态性．     

Estimates of MM type for the multivariate linear model

We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-estimation (Yohai 1987, [24]), we estimate the regression coefficients and the covariance matrix of the errors simultaneously. These estimates have both a high breakdown point and high asymptotic efficiency under Gaussian errors. We prove consistency and asymptotic normality assuming errors with an elliptical distribution. We describe an iterative algorithm for the numerical calculation of these estimates. The advantages of the proposed estimates over their competitors are demonstrated through both simulated and real data.     

PARMA模型参数最小绝对偏差(LAD)估计量的极限分布

在最优化理论基础上,采用相对较稳健的最小绝对偏差（LAD）估计方法,首先研究了周期自回归滑动平均（PARMA）模型参数估计问题,得到了PARMA模型LAD估计量的渐近分布.其次对该模型的LAD估计作了进一步的讨论,给出更一般假设条件下模型参数LAD估计量的渐近性质。     

Large sample properties of the mle and mcle for the natural parameter of a truncated exponential family

Summary Consider a truncated exponential family of absolutely continuous distributions with natural parameter θ and truncation parameter γ. Strong consistency and asymptotic normality are shown to hold for the maximum likelihood and maximum conditional likelihood estimates of θ with γ unknown. Moreover, these two estimates are also shown to have the same limiting distribution, coinciding with that of the maximum likelihood estimate for θ when γ is assumed to be known.     

A conditional least squares approach to PGARCH and PARMA–PGARCH time series estimation

In this Note, a conditional least squares (CLS) estimates for periodic GARCH (PGARCH) models with martingale difference centered squared innovations is developed. The approach is extended to the PARMA–PGARCH models. We establish the strong consistency and the asymptotic normality for our estimate.     

Asymptotic normality of spectral estimates

The asymptotic normality of some spectral estimates, including a functional central limit theorem for an estimate of the spectral distribution function, is proved for fourth-order stationary processes. In contrast to known results it is not assumed that all moments exist or that the process is linear. The data are allowed to be tapered. Using some recent results on the central limit theorem for stationary processes, corollaries are obtained for strong and φ-mixing sequences and linear transformations of martingale differences.     

回归曲线的加权正交级数估计

回归曲线的加权正交级数估计杨瑛（北京大学概率统计系，北京１００８７１）国家自然科学基金资助项目．１９８９年１２月３日收到．１９９０年１１月２２日收到第一次修改稿．１９９２年８月７日收到第二次修改稿．一、引论自１９６２年Ｃｅｎｃｏｖ［１］提出了密度函数...     

Statistical Analysis in Constant-Stress Accelerated Life Tests for Generalized Exponential Distribution Based on Adaptive Type-II Progressive Hybrid Censored Data

Based on adaptive type-II progressive hybrid censored data statistical analysis for constant-stress accelerated life test (CS-ALT) with products' lifetime following two-parameter generalized exponential (GE) distribution is investigated. The estimates of the unknown parameters and the reliability function are obtained through a new method combining the EM algorithm and the least square method. The observed Fisher information matrix is achieved with missing information principle, and the asymptotic unbiased estimate (AUE) of the scale parameter is also obtained. Confidence intervals (CIs) for the parameters are derived using asymptotic normality of the estimators and the percentile bootstrap (Boot-p) method. Finally, Monte Carlo simulation study is carried out to investigate the precision of the point estimates and interval estimates, respectively. It is shown that the AUE of the scale parameter is better than the corresponding two-step estimation, and the Boot-p CIs are more accurate than the corresponding asymptotic CIs.     

Modified Whittle estimation of multilateral models on a lattice

In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d?2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more generally, with standard convergence rate, faces two obstacles. One is the “edge effect”, which worsens with increasing d. The other is the possible difficulty of computing a continuous-frequency form of Whittle estimate or a time domain Gaussian maximum likelihood estimate, due mainly to the Jacobian term. This is especially a problem in “multilateral” models, which are naturally expressed in terms of lagged values in both directions for one or more of the d dimensions. An extension of the discrete-frequency Whittle estimate from the time series literature deals conveniently with the computational problem, but when subjected to a standard device for avoiding the edge effect has disastrous asymptotic performance, along with finite sample numerical drawbacks, the objective function lacking a minimum-distance interpretation and losing any global convexity properties. We overcome these problems by first optimizing a standard, guaranteed non-negative, discrete-frequency, Whittle function, without edge-effect correction, providing an estimate with a slow convergence rate, then improving this by a sequence of computationally convenient approximate Newton iterations using a modified, almost-unbiased periodogram, the desired asymptotic properties being achieved after finitely many steps. The asymptotic regime allows increase in both directions of all d dimensions, with the central limit theorem established after re-ordering as a triangular array. However our work offers something new for “unilateral” models also. When the data are non-Gaussian, asymptotic variances of all parameter estimates may be affected, and we propose consistent, non-negative definite estimates of the asymptotic variance matrix.     

On the asymptotic distribution of multivariate M-estimates

The asymptotic distribution of multivariate M-estimates is studied. It is shown that, in general, consistency leads to asymptotic normality and a Law of the Iterated Logarithm. The results are used to compute via matrix derivatives the asymptotic distribution of a class of estimates due to Maronna.     

Statistical analysis of grouped and censored observations

The asymptotic normality and asymptotic effectiveness of generalized Bayesian estimates are proved under less restrictive hypotheses.Translated from Staticheskie Metody, pp. 122–137, 1978.     

Consistent estimation in a linear regression problem with random errors in coefficients

We consider the linear regression model in the case when the independent variables are measured with errors, while the variances of the main observations depend on an unknown parameter. In the case of normally distributed replicated regressors we propose and study new classes of two-step estimates for the main unknown parameter. We find consistency and asymptotic normality conditions for first-step estimates and an asymptotic normality condition for second-step estimates. We discuss conditions under which these estimates have the minimal asymptotic variance.     

Asymptotic normality of the nearest neighbor hazard estimates

1.IntroductionSupposethatXI)')Xu,'beani.i.d.sequenceofrandomvariableswithdistributionfunctionF(x)anddensityfunctionf(x).TOestimatethedensityfunctionatxbasedonthefirstnobservations,thekerneltypeestimategiveswhereK')isakernelfunction,R.(x)isabandwidthsequenceandFi')isanestimateofF,usuallytakentobetheempiricaldistributionfunction.Formoredetails,see[21.Inmedicalapplications,itisoftenmoreimportanttoestimatethehazardfunctiondefinedbyA(x)~f(x)/(1--F(x)).IfXrepresentsalife-time,thenA(x)repre…     

On the weighted multivariate Wilcoxon rank regression estimate

Zhou (2010) introduced a multivariate Wilcoxon regression estimate which possesses some nice properties: computational ease, asymptotic normality and high efficiency. However, it is sensitive to the leverage points. To circumvent this problem, we propose a weighted multivariate Wilcoxon regression estimate. Under some regularity conditions, the asymptotic normality is established. We further study the robustness of the proposed estimate through the influence function. By properly choosing the weight functions, our results show that the corresponding estimate can have bounded influence function on both response and covariates.     

ASYMPTOTIC BEHAVIOR OF BOOTSTRAP SPECTRAL WINDOW ESTIMATION

ThisprojectissupportedbytheNationalNaturalScienceFoundationofChina.1.IntroductionLetX~{X(t);t=0,FI,12,'}beazerthmeantunitvariancestationaryGaussianprocess,theautocovariancefunction7(u)=EX(n u)X(n)satisfySupposethatthespectraldensityfunctionisf(A)andspectraldistributionfunctionisF(A)oftheprocessX,wherewerestrictAwithin11(if=[--x,7]).Fromtheassumptionsabove,wecaneasilyseethatthef(A)isjustaprobabilitydensityfunction,andF(A)isaprobabilitydistributionfunction.LetX(1),'tX(n)betheobserv…     

A sharpening of the remainder term in the higher-dimensional central limit theorem for multilinear rank statistics

A new approach to the asymptotic normality of the multivariate linear rank statistics is provided along with the Berry-Esséen and the Prohorov distance estimates for the remainder term in the convergence to normality.     

Propriétés asymptotiques d'estimateurs convergents des quantiles conditionnels

We state sufficient conditions for asymptotic normality of convergent estimates of the conditional quantiles, irrespective of data dependence, and give an application to α-mixing stationary processes, under optimal conditions. As an application, we use asymptotic normality to construct confidence bands for predictors based on nonparametric estimates of the conditional median.     

Ratio of Generalized Hill’s estimator and its asymptotic normality theory

We present a statistical process depending on a continuous time parameter τ whose each margin provides a Generalized Hill’s estimator. In this paper, the asymptotic normality of the finite-dimensional distributions of this family are completely characterized for τ > 1/2 when the underlying distribution function lies on the maximum domain of attraction. The ratio of two different margins of the statistical process characterizes entirely the whole domain of attraction. Its asymptotic normality is also studied. The results permit in general to build a new family of estimators for the extreme value index whose asymptotic properties can be easily derived. For example, we give a new estimate of the Weibull extreme value index and we study its consistency and its asymptotic normality.      

Window estimates of location and scale with applications to the cauchy distribution

Location and scale parameters are estimated via “window estimates”. The consistency and asymptotic normality of the estimates are established. The special case of the Cauchy distribution is considered, where the estimates are shown to have the same asymptotic distribution as the maximum-likelihood estimates. Additional applications are given for the Pearson type-VII distributions. The estimates have the advantages of ease of computation and high asymptotic efficiencies for certain heavy-tailed distributions.