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形状约束下的局部半参数效率界AI 解读

Consider the model y = x ′ β 0 + f * ( z ) + ε, where ε [d over =] N(0, σ 0 2 ). We calculate the smallest asymptotic variance that n 1/2 consistent regular ( n 1/2 CR) estimators of β 0 can have when the only information we possess about f * is that it has a certain shape. We focus on three particular cases: (i) when f * is homogeneous of degree r , (ii) when f * is concave, (iii) when f * is decreasing. Our results show that in the class of all n 1/2 CR estimators of β 0 ,…

带时间变换的 Copula 中的关联性AI 解读

We consider estimation of the cross ratio in Clayton's (1978) copula in which covariates are incorporated into the marginal distributions via semiparametric accelerated life regression models. Generalisations of Oakes' (1982, 1986) concordance estimating equations yield a closed form for the association parameter under right censoring. Joint inferences about covariate effects in the marginal models and the cross ratio are obtained with U-statistics, martingales and a…

非线性协方差分析的非参数模型与方法AI 解读

A fully nonparametric model for nonlinear analysis of covariance is proposed. The term nonlinear means that the covariate influences the response in a possibly nonlinear and nonpolynomial fashion, while the term fully nonparametric implies that the distributions for each factor level combination and covariate value are not restricted to comply with any parametric or semiparametric model. The possibility of different shapes of covariate effect in different factor level…

周期函数的非参数估计AI 解读

Motivated by applications to brightness data on periodic variable stars, we study nonparametric methods for estimating both the period and the amplitude function from noisy observations of a periodic function made at irregularly spaced times. It is shown that nonparametric estimators of period converge at parametric rates and attain a semiparametric lower bound which is the same if the shape of the periodic function is unknown as if it were known. Also, first-order…

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当前状态数据下的线性回归AI 解读

In survival analysis, a linear model often provides an adequate approximation after a suitable transformation of the survival times and possibly of the covariates. This article proposes a semiparametric regression method for estimating the regression parameter in the linear model without specifying the distribution of the random error, where the response variable is subject to so-called case 1 interval censoring. The method uses a constructed random-sieve likelihood and…

《计量经济学中的半参数方法》书评AI 解读

Semiparametric and nonparametric estimation have attracted a great deal of attention from statisticians and theoretical econometricians in the past decade. Various new models have been proposed, and new methods for estimating those models have been suggested. These new models and methods are scattered in various academic journals and are not easily accessible to other researchers. Moreover, the new methods are technical, not easily understood by nonexperts such as graduate…

多方程模型的半参数估计AI 解读

This paper proposes a semiparametric estimator for multiple equations multiple index (MEMI) models. Examples of MEMI models include several sample selection models and the multinomial choice model. The proposed estimator minimizes the average distance between the dependent variable unconditional and conditional on an index. The estimator is √N-consistent and asymptotically normally distributed. The paper also provides a Monte Carlo experiment to evaluate the finite-sample…

观测与删失时间依赖协变量的面板计数数据回归分析AI 解读

Summary Panel count data often occur in a long-term study where the primary end point is the time to a specific event and each subject may experience multiple recurrences of this event. Furthermore, suppose that it is not feasible to keep subjects under observation continuously and the numbers of recurrences for each subject are only recorded at several distinct time points over the study period. Moreover, the set of observation times may vary from subject to subject. In…

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半参数平均导数估计的 Edgeworth 展开AI 解读

A valid Edgeworth expansion is established for the limit distribution of density-weighted semiparametric averaged derivative estimates of single index models. The leading term that corrects the normal limit varies in magnitude, depending on the choice of bandwidth and kernel order. In general this term has order larger than the n−1/2 that prevails in standard parametric problems, but we find circumstances in which it is O(n−1/2), thereby extending the achievement of an n−1/2…

含滞后因变量的面板离散选择模型AI 解读

In this paper, we consider identification and estimation in panel data discrete choice models when the explanatory variable set includes strictly exogenous variables, lags of the endogenous dependent variable as well as unobservable individual-specific effects. For the binary logit model with the dependent variable lagged only once, Chamberlain (1993) gave conditions under which the model is not identified. We present a stronger set of conditions under which the parameters…

点过程强度函数中极点的估计AI 解读

Motivated by spatial data on earthquake epicentres, we consider the problem of estimating properties of poles in point-process intensity functions. Our methods are semiparametric, requiring only 'asymptotic' models for the intensity. They produce estimates of the locations and strengths of poles. Strength is expressed in terms of an exponent of regular variation, and is simply related to the correlation dimension of the underlying point process. It is argued that existing…

摘要暂缺

论剖面似然AI 解读

We show that semiparametric profile likelihoods, where the nuisance parameter has been profiled out, behave like ordinary likelihoods in that they have a quadratic expansion. In this expansion the score function and the Fisher information are replaced by the efficient score function and efficient Fisher information. The expansion may be used, among others, to prove the asymptotic normality of the maximum likelihood estimator, to derive the asymptotic chi-squared distribution…

通过稳健 Probit 模型进行风险评估及其毒理学应用AI 解读

Various frameworks have been suggested for assessing the risk associated with continuous toxicity outcomes. The first formulates the effect of exposure on the adverse effect via a simple normal model and then computes the risk function using tail probabilities from the standard normal distribution. Because this risk function depends heavily on the assumed model, it may be sensitive to model misspecification. Recently, a semiparametric approach that utilizes an alternative…

两样本多状态加速停留时间模型AI 解读

Many medical studies involve observations of patients experiencing multiple consecutive states during their follow-up. Since treatments may have differential effects on these states, comparison is of interest with respect to individual sojourn times. In this article I generalize the univariate accelerated failure time model for multistate processes and model the treatment effects as state-specific time scale changes. I propose a two-sample inference procedure that…

两样本多状态加速停留时间模型AI 解读

Many medical studies involve observations of patients experiencing multiple consecutive states during their follow-up. Since treatments may have differential effects on these states, comparison is of interest with respect to individual sojourn times. In this article I generalize the univariate accelerated failure time model for multistate processes and model the treatment effects as state-specific time scale changes. I propose a two-sample inference procedure that…

稳健 Probit 风险评估模型及其毒理学应用AI 解读

Various frameworks have been suggested for assessing the risk associated with continuous toxicity outcomes. The first formulates the effect of exposure on the adverse effect via a simple normal model and then computes the risk function using tail probabilities from the standard normal distribution. Because this risk function depends heavily on the assumed model, it may be sensitive to model misspecification. Recently, a semiparametric approach that utilizes an alternative…

半参数 ARCH 模型：估计函数方法AI 解读

We introduce the method of estimating functions to study the class of autoregressive conditional heteroscedasticity (ARCH) models. We derive the optimal estimating functions by combining linear and quadratic estimating functions. The resultant estimators are more efficient than the quasi-maximum likelihood estimator. If the assumption of conditional normality is imposed, the estimator obtained by using the theory of estimating functions is identical to that obtained by using…

面板数据半参数模型的统计推断AI 解读

In a longitudinal study, suppose that, for each subject, repeated measurements of the response variable and covariates are collected at a set of distinct, irregularly spaced time points. We consider a semiparametric model which relates the mean of the response variable at each time point proportionally to a function of a time-dependent covariate vector to analyse such panel data. Inference procedures for regression parameters are proposed without involving any nonparametric…

插补估计量的统计推断AI 解读

We derive an estimator of the asymptotic variance of both single and multiple imputation estimators. We assume a parametric imputation model but allow for non- and semiparametric analysis models. Our variance estimator, in contrast to the estimator proposed by Rubin (1987), is consistent even when the imputation and analysis models are misspecified and incompatible with one another.

稀有事件生存数据的一类加权对数秩检验AI 解读

In many epidemiological and medical follow-up studies, a majority of study subjects do not experience the event of interest during the follow-up period. An important example is the ongoing prostate, lung, colorectal, and ovarian cancer Screening trial of the National Cancer Institute. In such a situation, the widely used Gp family of weighted log-rank statistics essentially reduces to the special case of the (unweighted) log-rank statistics. We propose a simple modification…

协变量存在测量误差时的加性风险回归AI 解读

The additive hazards model specifies that the hazard function conditional on a set of covariates is the sum of an arbitrary baseline hazard function and a regression function of covariates. This article deals with the analysis of this semiparametric regression model with censored failure time data when covariates are subject to measurement error. We assume that the true covariate is measured on a randomly chosen validation set, whereas a Surrogate covariate (i.e., an…