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Efficient Bias Corrected Nonparametric Spectral Estimation

高效的偏差校正非参数谱估计

We consider estimation of a spectral density at a particular frequency. A linear model is developed based on the local second order behaviour of kernel spectral estimates and a second stage estimate is derived from this model. The estimate is shown to be asymptotically more efficient than the kernel estimate with the optimal, but unknown, bandwidth whenever the smoothing parameter is chosen so that it oversmooths the kernel estimate. A small-sample simulation study shows…

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Inadmissibility of the Maximum Likelihood Estimator in the Sequential Estimation of the Size of a Population

有限总体规模序贯估计中最大似然估计量的不可容许性

For the sequential estimation of the size of a finite population, the maximum likelihood estimator is a commonly used estimator but has no explicit expression. In this paper, it is shown that the maximum likelihood estimator is inadmissible. An estimator which is simple in form and dominates the maximum likelihood estimator asymptotically is proposed.

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An Interpolation Test for a Unit Root in the Presence of Drift

存在漂移时单位根的插值检验

This paper considers tests of the null hypothesis of a unit root in an observed time series against the alternative that the series is level stationary. Interpolation tests whose critical values depend upon the value of the drift parameter are compared to the standard Dickey-Fuller tests in Monte Carlo simulations. The interpolation test performs quite well especially against one-sided alternatives.

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Dynamic Linear Model Diagnostics

动态线性模型的诊断方法

In time series analysis using dynamic linear models, retrospective analysis involves the calculation of filtered, or smoothed, distributions for state parameters in the past. We develop and illustrate novel results that are useful in retrospective assessment of the influence of individual observations on such distributions. In particular, new and computationally simple filtering equations are derived for past state parameters based on leaving out one observation at a time,…

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Selection of Covariables in the Growth Curve Model

增长曲线模型中的协变量选择

The growth curve model introduced by Potthoff & Roy (1964) is a generalization of the multivariate analysis of variance model and is often employed to analyze longitudinal data, in which a single characteristic has been measured at p different occasions on each individual. Inferential problems of this model are studied by using analysis of covariance, i.e. partitioning the p measurements into the measurements of q response variables and p-q covariables. Rao (1965, 1966) and…

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Some Tests for Common Principal Component Subspaces in Several Groups

多组共同主成分子空间的若干检验

SUMMARY An approximate test, based on sample eigenprojections, is obtained for testing the hypothesis that the subspaces spanned by the first m principal components of several different covariance matrices are identical. This procedure can fairly easily be extended to an analogous test of hypothesis concerning correlation matrices. Extensions to a robust principal components analysis are also considered.

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A General Estimator of the Treatment Effect When the Data are Heavily Censored

严重删失数据下处理效应的一般估计量

A generalized Hodges-Lehmann type estimator for the treatment effect in the two-sample problem with right censoring, is proposed based on an inverse-quantile-type idea using truncated versions of the Kaplan-Meier estimators over the subspace where they are consistent. Its strong consistency and asymptotic normality can be obtained, under no conditions on the uninformative censorings, and the resulting variance is easily estimable from the data. In simulation studies the…

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A Note on Seber's Model for Ring-Recovery Data

关于Seber环志回收数据模型的说明

We provide simple explicit maximum-likelihood estimates for Seber's (1971) model for ring-recovery data, obtaining both the general solution, and the particular estimate satisfying Seber's constraint.

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An Accurate Method for Approximate Conditional and Bayesian Inference about Linear Regression Models from Censored Data

删失数据线性回归模型的精确近似条件推断与贝叶斯推断方法

A method to approximate marginal posterior distribution functions of regression coefficients and scale parameters is presented. It is based on a general asymptotic formula of DiCiccio, Field & Fraser (1990) for approximating marginal tail probabilities. In developing the method, it is shown that the accuracy of the approximation of DiCiccio et al. can be substantially increased in certain cases by using a suitable change of variables, and a general approach to deriving such…

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A Bayesian Significance Test of the Stationarity of Regression Parameters

回归参数稳定性的贝叶斯显著性检验

This paper presents a Bayesian significance test for stationarity of a regression equation using the highest posterior density credible set. In addition, a solution to the Behrens-Fisher problem is provided. From a Monte Carlo simulation study, it has been shown that the Bayesian significance test has stronger power than the Cusum and the Cusum of squares tests. The Bayesian significance test may be useful in detecting individual parameter nonstationarity.

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Bayesian Residual Analysis in the Presence of Censoring

存在删失时的贝叶斯残差分析

Use of the posterior distribution of the realized error terms for residual analysis in a linear model was advocated by Zellner (1975) and Zellner & Moulton (1985). The same idea was used by Chaloner & Brant (1988) to define outliers and calculate posterior probabilities of observations being outliers. This paper extends the same concept to other models, including regression models for lifetime data, by using an approach similar to that of Cox & Snell (1968). Residual plots…

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Relative Efficiency of the Log Rank Test Within a Multiplicative Intensity Model

乘法强度模型中对数秩检验的相对效率

For large-sample clinical trials with independent individuals randomly allocated to two treatment groups, in which survival times follow a log linear multiplicative intensity model with treatment group as one covariate, this paper calculates the asymptotic relative efficiency of the log rank test for treatment effect as compared with the optimal score test. The method is to exhibit the failure hazard intensity, not of proportional hazards form, obtained by ignoring all…

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Conditional Inference for Treatment and Error in Multivariate Analysis

多元分析中处理效应与误差的条件推断

Directional methods of conditional inference are applied to the multivariate analysis of variance. Exact and easily implemented tests are derived for treatment effect, error variance matrix, and treatment improvement. Each test provides a physical measure of the magnitude of the departure from expectation under the hypothesis and is conditional on the indicated direction of departure. For treatment effect the test provides a geometrically understandable alternative to tests…

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Kernel Density Estimation for Length Biased Data

长度偏倚数据的核密度估计

A new kernel density estimator for length biased data which derives from smoothing the nonparametric maximum likelihood estimator is proposed and investigated. It has various advantages over an alternative method suggested by Bhattacharyya, Franklin & Richardson (1988): it is necessarily a probability density, it is particularly better behaved near zero, it has better asymptotic mean integrated squared error properties and it is more readily extendable to related problems…

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A Fast Efficient Technique for the Estimation of Frequency

一种快速有效的频率估计技术

A technique is presented for the estimation of the frequency of a sinusoid in the presence of noise. The technique is based on fitting ARMA (2, 2) models iteratively, in a special way. The estimator is shown to be strongly consistent and as efficient as the least squares estimator of the frequency, or the periodogram maximizer. A simple accelerated version of the technique is shown to converge in a small number of iterations, the number depending on the accuracy of the…

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The Effect of Measurement Error

测量误差的影响

Measurement error causes the distribution generating data to differ from the distribution that is of substantive interest. To understand the effect of measurement error on the information produced by statistical procedures it is thus necessary to understand the distortions induced by measurement error. This paper sheds light on this by providing small error variance approximations to the distribution of variates subject to contamination by measurement error. These provide…

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Noninformative Priors for Inferences in Exponential Regression Models

指数回归模型推断的无信息先验

In the exponential regression model, inference concerning the regression parameter is notoriously difficult, even when using the Bayesian noninformative prior approach. The reference prior approach (Bernardo, 1979; Berger & Bernardo, 1989) is considered, and argued to yield very satisfactory inferences. Estimation and credible sets are considered in a specific example.

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