论文检索

一种半参数多重插补方法AI 解读

Summary In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional form for the data. The method is especially useful for non-standard problems, such as estimating the median when data are missing.

美国女性工资分布变化的动态：分位数回归方法AI 解读

This paper examines the female wage structure focusing on changes at different points in the wage distribution. Newly developed quantile regression methods are used in analysing data from the March Current Population Survey. The results show that while the most significant changes for the less skilled women took place at the bottom of the wage distribution, for the more skilled groups changes occurred at both ends of the distribution. Consequently, wage inequality decreased…

一般变换模型中具有根号n收敛率的非参数估计AI 解读

We propose simple estimators for the transformation function $\Lambda$ and the distribution function F of the error for the model $$\Lambda (Y) = \alpha + \mathbf{X} \mathbf{\beta} + \varepsilon.$$ It is proved that these estimators are consistent and can achieve the unusual $n^{-1/2}$ rate of convergence on any finite interval under some regularity conditions. We show that our estimators are more attractive than another class of estimators proposed by Horowitz. Interesting…

双重删失数据下的回归 M 估计AI 解读

The M-estimators are proposed for the linear regression model with random design when the response observations are doubly censored. The proposed estimators are constructed as some functional of a Campbell-type estimator $\hat{F}_n$ for a bivariate distribution function based on data which are doubly censored in one coordinate. We establish strong uniform consistency and asymptotic normality of $\hat{F}_n$ and derive the asymptotic normality of the proposed regression…

广义帕累托分布的数据拟合AI 解读

The generalized Pareto distribution (GPD) was introduced by Pickands to model exceedances over a threshold. It has since been used by many authors to model data in several fields. The GPD has a scale parameter ([sgrave] > 0) and a shape parameter (−∞ < k < ∞). The estimation of these parameters is not generally an easy problem. When k > 1, the maximum likelihood estimates do not exist, and when k is between 1/2 and 1, they may have problems. Furthermore, for k ≤ −1/2, second…

分布函数的反卷积AI 解读

We consider the estimation of a distribution function when observations from this distribution are contaminated by measurement error. The unknown distribution is modeled as a mixture of a finite number of known distributions. Model parameters can be estimated and confidence intervals constructed using well-known likelihood theory. We show that it is also possible to apply this approach to estimation of a unimodal distribution. An application is presented using data from a…

随机删失下累积风险与生存函数的精确Bootstrap置信限AI 解读

In clinical trials and other settings, confidence intervals for survival probabilities at a fixed point in time are often required. Typically, such intervals are constructed on the cumulative hazard scale; the quantiles required for computing these intervals are generally obtained using a first-order normal theory approximation. The appropriateness of such intervals for small sample sizes or under moderate to heavy censoring is suspect. We demonstrate that significant…

分布函数的反卷积估计AI 解读

We consider the estimation of a distribution function when observations from this distribution are contaminated by measurement error. The unknown distribution is modeled as a mixture of a finite number of known distributions. Model parameters can be estimated and confidence intervals constructed using well-known likelihood theory. We show that it is also possible to apply this approach to estimation of a unimodal distribution. An application is presented using data from a…

随机删失下累计风险与生存函数的准确自助置信限AI 解读

In clinical trials and other settings, confidence intervals for survival probabilities at a fixed point in time are often required. Typically, such intervals are constructed on the cumulative hazard scale; the quantiles required for computing these intervals are generally obtained using a first-order normal theory approximation. The appropriateness of such intervals for small sample sizes or under moderate to heavy censoring is suspect. We demonstrate that significant…

税收与转移支付归宿测度的统计推断AI 解读

We establish the asymptotic sampling distribution of general functions of quantile-based estimators computed from samples that are not necessarily independent. The results provide the statistical framework within which to assess the progressivity of taxes and benefits, their horizontal inequity, and the change in the inequality of income which they cause. By the same token, these findings characterise the sampling distribution of a number of popular indices of progressivity,…

单调处理响应AI 解读

This paper investigates what may be learned about treatment response when it is assumed that response functions are monotone, semimonotone, or concave-monotone. Nothing is assumed about the process of treatment selection and cross-individual restrictions on response are not imposed. The idea is to determine, for every member of the population, the response functions that pass through the realized (treatment, outcome) pair and that are consistent with the assumption imposed.…

工具变量模型中服从者结果分布的估计AI 解读

In Imbens and Ingrist (1994), Angrist, Imbens and Rubin (1996) and Imbens and Rubin (1997), assumptions have been outlined under which instrumental variables estimands can be given a causal interpretation as a local average treatment effect without requiring functional form or constant treatment effect assumptions. We extend these results by showing that under these assumptions one can estimate more from the data than the average causal effect for the subpopulation of…

样本均值分布的两种渐近展开之间的关系及应用AI 解读

Although the cumulative distribution function may be differentiated to obtain the corresponding density function, whether or not a similar relationship exists between their asymptotic expansions remains a question. We provide a rigorous argument to prove that Lugannani and Rice's asymptotic expansion for the cumulative distribution function of the mean of a sample of i.i.d. observations may be differentiated to obtain Daniels's asymptotic expansion for the corresponding…

This paper presents two approaches for qualitative, quantitative and comparative concepts of skewness to be defined with respect to the spatial median for multivariate distributions. They extend the known quantile-based notions defined for real distributions. The main tool for such extensions consists of a family of central parts that provide suitable generalizations of the real interquantile intervals.

回归中的子序列方法AI 解读

We extend subseries methods for stationary sequences to the regression setting. To estimate sampling distributions, the subseries approach computes the statistic of interest on all possible subseries of a shorter length than the original series, and uses the distribution of these replicates to mimic the distribution of the original statistic. For proper choice of subseries length, the regression parameters can be estimated to order O(n -2/3), thus improving on the normal…

条件柯尔莫哥洛夫检验AI 解读

This paper introduces a conditional Kolmogorov test of model specification for parametric models with covariates (regressors). The test is an extension of the Kolmogorov test of goodness-of-fit for distribution functions. The test is shown to have power against 1/[square root of n] local alternatives and all fixed alternatives to the null hypothesis. A parametric bootstrap procedure is used to obtain critical values for the test.

标准化统计量分布函数的估计AI 解读

Summary For estimating the distribution of a standardized statistic, the bootstrap estimate is known to be local asymptotic minimax. Various computational techniques have been developed to improve on the simulation efficiency of uniform resampling, the standard Monte Carlo approach to approximating the bootstrap estimate. Two new approaches are proposed which give accurate yet simple approximations to the bootstrap estimate. The second of the approaches even improves the…

离散参数未知的广义线性模型中三种经典检验的局部功效AI 解读

In this paper we obtain asymptotic expansions for the nonnull distribution functions of the likelihood ratio, score and Wald test statistics in exponential family generalised linear models under Pitman alternatives assuming unknown dispersion, thus generalising some of the results in Cordeiro, Botter & Ferrari (1994).

广义加权 Cramér–von Mises 距离估计量AI 解读

SUMMARY Generalised weighted Cramer-von Mises distance estimators in an arbitrary model with a k-dimensional parameter vector are investigated. The distance function is defined as a function G of model-based residuals for a specified target model F and the empirical cumulative distribution function F, over the real line, invoking a weight function w. It is shown that the estimator is Fisher consistent, asymptotically multivariate normal, and nearly efficient with desirable…

By approximating the classical product-limit estimator of a distribution function with an average of iid random variables, we derive sufficient and necessary conditions for the rate of (both strong and weak) uniform convergence of the product-limit estimator over the whole line. These findings somehow fill a longstanding gap in the asymptotic theory of survival analysis. The result suggests a natural way of estimating the rate of convergence. We also prove a related…

基于排序集样本的分布函数非参数最大似然估计的渐近性质AI 解读

We show that the nonparametric maximum likelihood estimator (NPMLE) of a distribution function based on balanced ranked set samples is consistent, converges weakly to a Gaussian process and is asymptotically efficient. The covariance function of the limiting process is described in terms of the solution to a Fredholm integral equation of the second kind.

ARIMA模型中季节移动平均单位根的检验AI 解读

Procedures to test for a unit root in the seasonal moving average (MA) operator for seasonal autoregressive integrated moving average (ARIMA) models are examined. Such procedures are useful to distinguish deterministic from stochastic seasonal patterns. The simple (integrated) seasonal MA(1) model Z t ≡ Y t - Yt-s = (1 - θB s)ϵt, where {Y t} is the observed seasonal series and {ϵt} is a white noise sequence, is considered, and tests for H 0: θ = 1 versus H 1: θ < 1 are…

超光滑测量误差下的密度估计AI 解读

The problem is to estimate the probability density of a random variable contaminated by an independent measurement error. I explore one of the worst-case scenario when the characteristic function of this measurement error decreases exponentially and thus optimal estimators converge only with logarithmic rate. The particular example of such measurement error is any random variable contaminated by normal, Cauchy, or another stable random variable. For this setting and circular…

平均导数分位数回归AI 解读

For fixed $\alpha \epsilon (0, 1)$, the quantile regression function gives the $\alpha$th quantile $\theta_{\alpha}(\mathbf{x})$ in the conditional distribution of a response variable Y given the value $\mathbf{X} = \mathbf{x}$ of a vector of covariates. It can be used to measure the effect of covariates not only in the center of a population, but also in the upper and lower tails. A functional that summarizes key features of the quantile specific relationship between X and…