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Using calibration weighting to adjust for nonresponse under a plausible model

在合理响应模型下用校准加权调整无响应

When we estimate the population total for a survey variable or variables, calibration forces the weighted estimates of certain covariates to match known or alternatively estimated population totals called benchmarks. Calibration can be used to correct for sample-survey nonresponse, or for coverage error resulting from frame undercoverage or unit duplication. The quasi-randomization theory supporting its use in nonresponse adjustment treats response as an additional phase of…

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Additive partial linear models with measurement errors

含测量误差的加性部分线性模型

We consider statistical inference for additive partial linear models when the linear covariate is measured with error. We propose attenuation-to-correction and simulation-extrapolation, simex, estimators of the parameter of interest. It is shown that the first resulting estimator is asymptotically normal and requires no undersmoothing. This is an advantage of our estimator over existing backfitting-based estimators for semiparametric additive models which require…

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Generalized varying coefficient models for longitudinal data

纵向数据的广义变系数模型

We propose a generalization of the varying coefficient model for longitudinal data to cases where not only current but also recent past values of the predictor process affect current response. More precisely, the targeted regression coefficient functions of the proposed model have sliding window supports around current time t. A variant of a recently proposed two-step estimation method for varying coefficient models is proposed for estimation in the context of these…

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Conditional properties of unconditional parametric bootstrap procedures for inference in exponential families

指数族推断中无条件参数自助法的条件性质

Higher-order inference about a scalar parameter in the presence of nuisance parameters can be achieved by bootstrapping, in circumstances where the parameter of interest is a component of the canonical parameter in a full exponential family. The optimal test, which is approximated, is a conditional one based on conditioning on the sufficient statistic for the nuisance parameter. A bootstrap procedure that ignores the conditioning is shown to have desirable conditional…

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Improving the efficiency of the log-rank test using auxiliary covariates

利用辅助协变量提高对数秩检验的效率

Under the assumption of proportional hazards, the log-rank test is optimal for testing the null hypothesis ⁠, where denotes the logarithm of the hazard ratio. However, if there are additional covariates that correlate with survival times, making use of their information will increase the efficiency of the log-rank test. We apply the theory of semiparametrics to characterize a class of regular and asymptotically linear estimators for when auxiliary covariates are incorporated…

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Improving Semiparametric Estimation by Using Surrogate Data

利用替代数据改进半参数估计

Summary The paper considers estimating a parameter β that defines an estimating function U(y, x, β) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for β depends critically on correctly specifying the conditional expectation of U given the surrogate and the covariate.…

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Non-Parametric Inference for Clustered Binary and Count Data when Only Summary Information is Available

仅有汇总信息时聚类二元与计数数据的非参数推断

Summary Data in the form of pairs (X,Y), where the response Y is a count, arise in many applications, including problems involving stratified or two-stage sampling. Such data are often analysed by using random-effects models, where the distribution of Y, conditional on X and on an unobserved random parameter Θ, is taken to be either binomial or Poisson, and the distribution of Θ is connected through a link function to a random effect. The latter is sometimes supposed to be…

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Robust Estimation in the Normal Mixture Model Based on Robust Clustering

基于稳健聚类的正态混合模型稳健估计

Summary We introduce a robust estimation procedure that is based on the choice of a representative trimmed subsample through an initial robust clustering procedure, and subsequent improvements based on maximum likelihood. To obtain the initial trimming we resort to the trimmed k-means, a simple procedure designed for finding the core of the clusters under appropriate configurations. By handling the trimmed data as censored, maximum likelihood estimation provides in each step…

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Modelling Sparse Generalized Longitudinal Observations with Latent Gaussian Processes

用潜在高斯过程建模稀疏广义纵向观测

Summary In longitudinal data analysis one frequently encounters non-Gaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson or of another discrete type or could be continuous. The timings of the repeated measurements are often sparse and irregular. We introduce a latent Gaussian process model for such data, establishing a connection to functional data analysis. The functional methods proposed are…

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Modelling Multivariate Volatilities via Conditionally Uncorrelated Components

利用条件不相关分量建模多元波动率

Summary We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem into several lower dimensional subproblems. Consistency for the…

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Isotropic Spectral Additive Models of the Covariogram

协方差图的各向同性谱加性模型

Summary A class of additive covariance models of an isotropic random process is proposed, motivated by the spectral representation of the covariance function. Model parameters are estimated by using a special case of the minimum norm quadratic estimation estimator, whose asymptotic moments have convenient expressions in terms of spectral densities. Fitting a model in this class is equivalent to fitting an additive model of the spectral density. The class of spectral additive…

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Local likelihood estimation of truncated regression and its partial derivatives: Theory and application

截断回归及其偏导数的局部似然估计:理论与应用

In this paper we propose a very flexible estimator in the context of truncated regression that does not require parametric assumptions. To do this, we adapt the theory of local maximum likelihood estimation. We provide the asymptotic results and illustrate the performance of our estimator on simulated and real data sets. Our estimator performs as well as the fully parametric estimator when the assumptions for the latter hold, but as expected, much better when they do not…

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Testing for structural change in regression quantiles

回归分位数的结构变化检验

Most studies in the structural change literature focus solely on the conditional mean, while under various circumstances, structural change in the conditional distribution or in conditional quantiles is of key importance. This paper proposes several tests for structural change in regression quantiles. Two types of statistics are considered, namely, a fluctuation type statistic based on the subgradient and a Wald type statistic, based on comparing parameter estimates obtained…

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Asymptotic and bootstrap tests for linearity in a TAR-GARCH(1,1) model with a unit root

含单位根的 TAR-GARCH(1,1) 模型:线性性的渐近与自助法检验

This paper derives the limiting distribution of the Lagrange Multiplier (LM) test for threshold nonlinearity in a TAR model with GARCH errors when one of the regimes contains a unit root. It is shown that the asymptotic distribution is nonstandard and depends on nuisance parameters that capture the degree of conditional heteroskedasticity and non-Gaussian nature of the process. We propose a bootstrap procedure for approximating the exact finite-sample distribution of the…

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A joint serial correlation test for linear panel data models

线性面板数据模型的联合序列相关检验

This paper proposes a joint error serial correlation test to be applied to linear panel data models after generalised method of moments estimation. This new test is an alternative inferential tool to both the m2 test of [Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277-297] and the overidentifying restrictions test. The proposed test, called the test,…

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The wild bootstrap, tamed at last

野生自助法:拒绝概率误差的改进

The wild bootstrap is studied in the context of regression models with heteroskedastic disturbances. We show that, in one very specific case, perfect bootstrap inference is possible, and a substantial reduction in the error in the rejection probability of a bootstrap test is available much more generally. However, the version of the wild bootstrap with this desirable property is without the skewness correction afforded by the currently most popular version of the wild…

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Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large

个体数与时期数均较大时固定效应空间动态面板的准极大似然估计

This paper investigates the asymptotic properties of quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects, when both the number of individuals n and the number of time periods T are large. We consider the case where T is asymptotically large relative to n, the case where T is asymptotically proportional to n, and the case where n is asymptotically large relative to T. In the case where T is asymptotically large relative to n, the estimators…

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Pseudo-likelihood estimation and bootstrap inference for structural discrete Markov decision models

结构离散马尔可夫决策模型的伪似然估计与自助推断

This paper analyzes the higher-order properties of the estimators based on the nested pseudo-likelihood (NPL) algorithm and the practical implementation of such estimators for parametric discrete Markov decision models. We derive the rate at which the NPL algorithm converges to the MLE and provide a theoretical explanation for the simulation results in Aguirregabiria and Mira [Aguirregabiria, V., Mira, P., 2002. Swapping the nested fixed point algorithm: A class of…

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Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities

满足有限个矩不等式的部分识别参数置信集

This paper proposes a computationally simple way to construct confidence sets for a parameter of interest in models comprised of moment inequalities. Building on results from the literature on multivariate one-sided tests, I show how to test the hypothesis that any particular parameter value is logically consistent with the maintained moment inequalities. The associated test statistic has an asymptotic chi-bar-square distribution, and can be inverted to construct an…

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Partial identification and testable restrictions in multi-unit auctions

多单位拍卖的部分识别与可检验限制

Bidders' values in discriminatory and uniform-price auctions are not necessarily point-identified under the assumptions of equilibrium bidding and independent private values, but meaningful policy analysis can proceed from bounds on bidder values. This paper provides upper and lower bounds on the set of values that can rationalize a given distribution of bids, under the additional (and standard) assumption of non-increasing marginal values. Novel testable implications of the…

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The limit distribution of the estimates in cointegrated regression models with multiple structural changes

具有多次结构变化的协整回归估计量的极限分布

We study estimation and inference in cointegrated regression models with multiple structural changes allowing both stationary and integrated regressors. Both pure and partial structural change models are analyzed. We derive the consistency, rate of convergence and the limit distribution of the estimated break fractions. Our technical conditions are considerably less restrictive than those in Bai et al. [Bai, J., Lumsdaine, R.L., Stock, J.H., 1998. Testing for and dating…

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Model-Independent Estimates of Dark Matter Distributions

暗物质分布的模型无关估计

A new nonparametric method is described to estimate the distribution of mass within spherical galaxies. The problem of estimating the mass, M(r), within radius r is converted into a problem of estimating a regression function nonparametrically, subject to shape restrictions. We represent the restrictions by the interception of quadratic cones and use the second-order cone programming to estimate the unknown parameters. We establish asymptotic results that are used to…

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A Directional Model for the Estimation of the Rotation Axes of the Ankle Joint

踝关节旋转轴估计的方向统计模型

This article is motivated by the estimation of the directions of the two rotation axes of the ankle. These axes carry information on individual ankles; they are useful in the construction of biomechanical models and the treatment of orthopedic problems. In biomechanics, the rotation axes of the ankle often are estimated using optimization techniques. This work investigates a statistical model for carrying out the estimation. The data set for analysis is a time-ordered…

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Binary Time Series Modeling With Application to Adhesion Frequency Experiments

二元时间序列建模及其在黏附频率实验中的应用

Repeated adhesion frequency assay is the only published method for measuring the kinetic rates of cell adhesion. Cell adhesion plays an important role in many physiological and pathological processes. Traditional analysis of adhesion frequency experiments assumes that the adhesion test cycles are independent Bernoulli trials. This assumption can often be violated in practice. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating…

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