论文检索

指数响应模型中的极大似然估计AI 解读

Exponential response models are a generalization of logit models for quantal responses and of regression models for normal data. In an exponential response model, $\{F(\theta): \theta \in \Theta\}$ is an exponential family of distributions with natural parameter $\theta$ and natural parameter space $\Theta \subset V$, where $V$ is a finite-dimensional vector space. A finite number of independent observations $S_i, i \in I$, are given, where for $i \in I, S_i$ has…

正态混合分布抽样下的单样本 t 检验AI 解读

For the one-sample $t$-test a new form of the exact distribution of the test statistic $t^2$ is obtained when sampling from a distribution which is a mixture of two normal distributions. A numerical example is provided to show that the size of the test can differ greatly when sampling from distributions having the same skewness and kurtosis. Contours of equal size are plotted for a particular case in a certain cross section of the parameter space.

部分替换抽样单位的重复抽样AI 解读

This paper is concerned with the minimum variance estimation of a time-dependent population mean, assuming that one is restricted to the case of linear unbiased estimators. A number of results are given for a new rotation sampling model (RSM), in which unequal sample sizes are used on each occasion. Also results corresponding to the special case of sampling with a fixed sample size on all the occasions are derived. Finally the optimum structure of the suggested model is…

基于 U 统计量的位置估计量AI 解读

Let $X_1, \cdots, X_n$ be i.i.d. $F$, and estimate the median of $F$ by the median $T_\beta$ of $\beta X_i + (1 - \beta)X_j, i \neq j$, where $\beta$ is a fixed positive constant. Then $T_\beta$ is the solution of a $U$-statistic equation from which its asymptotic normality is readily derived. The asymptotic relative efficiency of $T_\beta$ is computed for a few cdfs $F$ and seen to be reasonably high for unintuitive choices such as $\beta = .9, \beta = 2$, and also to be…

单参数指数族中线性估计量的可容许性AI 解读

For estimating the mean in the one parameter exponential family with quadratic loss, Karlin (1958) gave sufficient conditions for admissibility of estimators of the form $aX$. Later, Ping (1964) and Gupta (1966) gave sufficient conditions for admissibility of estimators of the form $aX + b$ for the same problem. Zidek (1970) gave sufficient conditions for the admissibility of $X$ for estimating an arbitrary piecewise continuous function of the parameter, say $\gamma(\theta)$,…

未知协方差与任意二次损失下正态均值向量的极小极大估计AI 解读

Let $X$ be an observation from a $p$-variate normal distribution $(p \geqq 3)$ with mean vector $\theta$ and unknown positive definite covariance matrix $\not\Sigma$. It is desired to estimate $\theta$ under the quadratic loss $L(\delta, \theta, \not\Sigma) = (\delta - \theta)^tQ(\delta - \theta)/\operatorname{tr} (Q\not\Sigma)$, where $Q$ is a known positive definite matrix. Estimators of the following form are considered: $\delta^c(X, W) = (I - c\alpha Q^{-1}W^{-1}/(X^tW^{-1}X))X,$…

方差分析混合模型中最大似然估计的渐近性质AI 解读

We show that in the mixed model of the analysis of variance, there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal, and efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix. These results follow directly by an application of a general result of Weiss (1971, 1973) concerning maximum likelihood estimates. This problem differs from standard problems in that we do not have independent, identically…

时间序列的预测函数与均值估计函数AI 解读

Let $T \subseteqq I$ be sets of real numbers. Let $\{Y(t): t \in I\}$ be a real time series whose mean is an unknown element of a known class of functions on $I$ and whose covariance kernel $k$ is assumed known. For each fixed $s \in I, Y(s)$ is predicted by a minimum mean square error unbiased linear predictor $\hat{Y}(s)$ based on $\{Y(t): t \in T\}$. If $\hat{y}(s)$ is the evaluation of $\hat{Y}(s)$ given a set of observations $\{Y(t) = g(t): t \in T\}$, then the function…

ARMA 模型新息方差的稳健区间估计AI 解读

For the autoregressive-moving average time series model, the normal theory procedure for setting confidence intervals for the error variance is not robust against nonnormality. This paper proposes three asymptotically robust techniques: they are a "standard-error" procedure, an analog of Box's simple data splitting technique, and the jackknife procedure. The large sample distribution of each of these techniques is derived.

依赖误差回归模型中稳健估计量的行为AI 解读

This paper proves the asymptotic linearity in the regression parameter of a class of linear rank statistics when errors in the regression model are strictly stationary and strongly mixing. Besides this, several other weak convergence results are proved which yield the asymptotic normality of $L$ and $M$ estimators of the regression parameter under the above dependent structure. All these results are useful in studying the effect of the above dependence on the asymptotic…

线性秩统计量的收敛与余项AI 解读

A new approach to the asymptotic normality of simple linear rank statistics for the regression case studied earlier by Hajek (1968) is provided along with the estimation of the remainder term in the approximation to normality.

位置 M 估计量渐近方差的上界AI 解读

If $X_1, \cdots, X_n$ is a random sample from $F(x - \theta)$, where $F$ is an unknown member of a specified class $\mathscr{F}$ of approximately normal symmetric distributions, then an $M$-estimator of the unknown location parameter $\theta$ is obtained by solving the equation $\sum^n_{i=1} \psi(X_i - \hat{\theta}_n) = 0$ for $\hat{\theta}_n$. A suitable measure of the robustness of the $M$-estimator is $\sup \{V(\psi, F): F \in \mathscr{F}\}$, where $V(\psi, F) = \int \psi^2 dF/(\int \psi' dF)^2$…

原假设与备择下简单线性秩统计量的收敛速度AI 解读

Convergence rates for distributions of simple linear rank statistics are investigated. Both the null hypothesis and near alternatives are considered. The method of proof consists in approximating the characteristic function of the statistic by that of a sum of independent random variables and then applying standard tools.

一致的非参数回归AI 解读

Let $(X, Y)$ be a pair of random variables such that $X$ is $\mathbb{R}^d$-valued and $Y$ is $\mathbb{R}^{d'}$-valued. Given a random sample $(X_1, Y_1), \cdots, (X_n, Y_n)$ from the distribution of $(X, Y)$, the conditional distribution $P^Y(\bullet \mid X)$ of $Y$ given $X$ can be estimated nonparametrically by $\hat{P}_n^Y(A \mid X) = \sum^n_1 W_{ni}(X)I_A(Y_i)$, where the weight function $W_n$ is of the form $W_{ni}(X) = W_{ni}(X, X_1, \cdots, X_n), 1 \leqq i \leqq n$.…

无限方差自回归过程最小二乘估计的渐近行为AI 解读

Let $y_t$ be an order $p$ autoregressive process of the form $y_t + \sum^p_{s=1} \beta_s y_{t-s} = u_t$, where the $u_t$'s are i.i.d. variables with a symmetric distribution $F$ such that $E \log^+ |u_t| < \infty$. For the Yule-Walker version $\beta_T^\ast$ of the least-squares estimate of $\beta = (\beta_1,\cdots, \beta_p)$, it is shown that $T^\frac{1}{2}(\beta_T^\ast - \beta)$ is bounded in probability.

平稳时间序列谱密度的一种估计量AI 解读

An estimator for the spectral density of a stationary time sequence is introduced. The form of the estimator is motivated by the analogy with the summability theory of Fourier series. A theorem relating to rate of consistency is proved. The effect of a mean correction is considered.

密度估计的平均积分平方误差性质AI 解读

The rate at which the mean integrated square error decreases as sample size increases is evaluated for general $L^1$ kernel estimates and for the Fourier integral estimate for a probability density. The rates are compared to that of the minimum M.I.S.E.; the Fourier integral estimate is found to be asymptotically optimal.

最小方差无偏估计量的渐近效率AI 解读

Consider a regular $p$-dimensional exponential family such that either the distributions are concentrated on a lattice or they have a component whose $k$-fold convolution has a bounded density with respect to Lebesgue measure. Then, if a parametric function has an unbiased estimator, the minimum variance unbiased estimators are asymptotically equivalent to the maximum likelihood estimators; and, hence, are asymptotically efficient. Examples are given to show that a condition…

单调似然比族中经验贝叶斯单调多重决策问题的改进收敛率AI 解读

In the context of the two-action, linear loss, exponential family multiple decision problem, Van Houwelingen (1973), (1976) has shown that faster rates of convergence are deducible for monotone empirical Bayes procedures than result from application of the bound established for general empirical Bayes procedures by Johns and Van Ryzin (1972). This note generalizes a (1973) Van Houwelingen bound to arbitrary $k$-action, monotone loss, MLR family multiple decision problems. An…

可交换随机变量的一种正依赖概念AI 解读

An $n$-variate distribution function is said to be positive dependent by mixture (PDM) if it is a mixture of independent $n$-variate distributions with equal marginals. PDM distributions arise in various contexts of reliability and other areas of statistics. We give a necessary and sufficient condition, by means of independent random variables, for an $n$-variate distribution function to be PDM. The distributions and the expectations of the order statistics of PDM and of…

次序统计量函数的重对数律AI 解读

A general law of the iterated logarithm for linear combinations of order statistics is proved. The key tools are (1) iterated logarithm convergence of the uniform empirical process $U_n$ in $\rho_q$-metrics due to B. R. James and (2) almost sure "nearly linear" bounds for the empirical distribution function. A law of the iterated logarithm for the quantile process is also established.

Glivenko–Cantelli定理与次序统计量函数的强大数定律AI 解读

A strengthened version of the Glivenko-Cantelli theorem for the uniform empirical distribution function is proved. The strengthened Glivenko-Cantelli theorem is used to establish strong laws of large numbers for linear functions of order statistics.

线性回归中 M 估计与 R 估计的渐近关系AI 解读

Let $\hat{\mathbf{\Delta}}_M$ be an $M$-estimator (maximum-likelihood type estimator) and $\hat{\mathbf{\Delta}}_R$ be an $R$-estimator (rank estimator) of the parameter $\mathbf{\Delta} = (\Delta_1,\cdots, \Delta_p)$ in the linear regression model $X_{Ni} = \sum^p_{j=1} \Delta_jc_{ji} + e_i, i = 1,\cdots, N$. The asymptotic distribution of $\hat\mathbf{\Delta}_M - \hat\mathbf{\Delta}_R$ is derived for $p$ fixed and $N \rightarrow \infty,$ under some assumptions on the…

参数模型的最小 Hellinger 距离估计AI 解读

This paper defines and studies for independent identically distributed observations a new parametric estimation procedure which is asymptotically efficient under a specified regular parametric family of densities and is minimax robust in a small Hellinger metric neighborhood of the given family. Associated with the estimator is a goodness-of-fit statistic which assesses the adequacy of the chosen parametric model. The fitting of a normal location-scale model by the new…