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从数据统计到知识统计AI 解读

Increasingly, datasets are so large they must be summarized in some fashion so that the resulting summary dataset is of a more manageable size, while still retaining as much knowledge inherent to the entire dataset as possible. One consequence of this situation is that the data may no longer be formatted as single values such as is the case for classical data, but rather may be represented by lists, intervals, distributions, and the like. These summarized data are examples…

利用贝叶斯模型选择识别微阵列中的差异表达基因AI 解读

DNA microarrays open up a broad new horizon for investigators interested in studying the genetic determinants of disease. The high throughput nature of these arrays, where differential expression for thousands of genes can be measured simultaneously, creates an enormous wealth of information, but also poses a challenge for data analysis because of the large multiple testing problem involved. The solution has generally been to focus on optimizing false-discovery rates while…

含删失生存数据的临床试验样本量再估计AI 解读

A flexible design with updating of sample size in clinical trials is proposed for censored survival data. Patients enter trials serially and are subject to random loss to follow-up. The statistical inference is based on a weighted average of the linear rank statistics, where the weight function at each look depends on prior observed data. A stopping rule is devised to allow early termination and acceptance of the null hypothesis when the experimental treatment offers no…

计量经济学的远见与影响：约翰·丹尼斯·萨根AI 解读

Denis Sargan's intellectual influence in econometrics is discussed and some of his visions for the future of econometrics are considered in this memorial article. One of Sargan's favorite topics in econometric theory was finite sample theory, including both exact theory and various types of asymptotic expansions. We provide some summary discussion of asymptotic expansions of the type that Sargan developed in this field and give explicit representations of Sargan's formula…

丹尼斯·萨根：若干历史视角AI 解读

I attempt to present Denis Sargan's work in some kind of historical perspective, in two ways. First, I discuss some previous members of the Tooke Chair of Economic Science and Statistics, which was founded in 1859 and which Sargan held. Second, I discuss one of his articles, “Asymptotic Theory and Large Models,” in relation to modern preoccupations with semiparametric econometrics.This research was supported by a Leverhulme Trust Personal Research Professorship and ESRC…

计量经济学的当前问题：一场个人观点演讲AI 解读

Forty-five years ago when I started to study econometrics seriously I was full of confidence that the application of mathematics and statistical methodology would soon lead to a better understanding of the economic system, and, ultimately, to better public and private policies. Now, with the world economy poised on the edge of a world slump and Eastern Europe and the undeveloped world in turmoil my general hope seems optimistic. Even within econometrics, experience proves…

贝叶斯聚类与乘积分割模型AI 解读

Summary We present a decision theoretic formulation of product partition models (PPMs) that allows a formal treatment of different decision problems such as estimation or hypothesis testing and clustering methods simultaneously. A key observation in our construction is the fact that PPMs can be formulated in the context of model selection. The underlying partition structure in these models is closely related to that arising in connection with Dirichlet processes. This allows…

通过模拟退火进行经典模型选择AI 解读

Summary The classical approach to statistical analysis is usually based upon finding values for model parameters that maximize the likelihood function. Model choice in this context is often also based on the likelihood function, but with the addition of a penalty term for the number of parameters. Though models may be compared pairwise by using likelihood ratio tests for example, various criteria such as the Akaike information criterion have been proposed as alternatives…

时间序列误差下非参数回归推断的差分方法AI 解读

Summary We show that difference-based methods can be used to construct simple and explicit estimators of error covariance and autoregressive parameters in nonparametric regression with time series errors. When the error process is Gaussian our estimators are efficient, but they are available well beyond the Gaussian case. As an illustration of their usefulness we show that difference-based estimators can be used to produce a simplified version of time series…

时间序列的数据倾斜方法AI 解读

Summary We develop a general methodology for tilting time series data. Attention is focused on a large class of regression problems, where errors are expressed through autoregressive processes. The class has a range of important applications and in the context of our work may be used to illustrate the application of tilting methods to interval estimation in regression, robust statistical inference and estimation subject to constraints. The method can be viewed as ‘empirical…

具有平滑随机效应的函数型准似然回归模型AI 解读

Summary We propose a class of semiparametric functional regression models to describe the influence of vector-valued covariates on a sample of response curves. Each observed curve is viewed as the realization of a random process, composed of an overall mean function and random components. The finite dimensional covariates influence the random components of the eigenfunction expansion through single-index models that include unknown smooth link and variance functions. The…

通过扰动对称性生成分布：侧重多元偏斜t分布AI 解读

Summary A fairly general procedure is studied to perturb a multivariate density satisfying a weak form of multivariate symmetry, and to generate a whole set of non-symmetric densities. The approach is sufficiently general to encompass some recent proposals in the literature, variously related to the skew normal distribution. The special case of skew elliptical densities is examined in detail, establishing connections with existing similar work. The final part of the paper…

极端值簇的统计推断AI 解读

Summary Inference for clusters of extreme values of a time series typically requires the identification of independent clusters of exceedances over a high threshold. The choice of declustering scheme often has a significant effect on estimates of cluster characteristics. We propose an automatic declustering scheme that is justified by an asymptotic result for the times between threshold exceedances. The scheme relies on the extremal index, which we show may be estimated…

用不完美模型解释统计证据：稳健调整似然函数AI 解读

Summary The strength of statistical evidence is measured by the likelihood ratio. Two key performance properties of this measure are the probability of observing strong misleading evidence and the probability of observing weak evidence. For the likelihood function associated with a parametric statistical model, these probabilities have a simple large sample structure when the model is correct. Here we examine how that structure changes when the model fails. This leads to…

摘要暂缺

摘要暂缺

长记忆参数的高阶核半参数 M 估计AI 解读

Econometric interest in the possibility of long memory has developed as a flexible alternative to, or compromise between, the usual short memory or unit root prescriptions, for example in the context of modelling cointegrating or other relationships and in describing the dependence structure of nonlinear functions of financial returns. Semiparametric methods of estimating the memory parameter can avoid bias incurred by misspecification of the short memory component. We…

半参数弱工具变量回归及其风险—收益权衡应用AI 解读

We extend the local-to-zero analysis of models with weak instruments to models with estimated instruments and regressors and with higher-order dependence between instruments and disturbances. This framework is applicable to linear models with expectation variables that are estimated nonparametrically, such as the risk-return tradeoff in finance and the effect of inflation uncertainty on real economic activity. Our simulation evidence suggests that Lagrange multiplier…

远期汇率无偏性检验：水平回归与收益率回归AI 解读

Several recent empirical studies have been forced to reject exact 1:1 cointegration between spot and forward exchange rates. Theoretically, this is shown to provide a possible explanation for the puzzling negative estimates reported from spot-return-forward-premium regressions. In particular, the coefficient in this regression has a unit root component in its limit distribution that imparts a bias and skewness to the estimator. Simulations are used to demonstrate how even…

内生解释变量下删失模型的推断AI 解读

This paper analyzes the linear regression model y = x + with a conditional median assumption Med( j z) = 0 where z is a vector of exogenous random variables. Added complication arise due to the censoring of the outcome y. We treat the censored model as a model with interval-observed outcomes thus obtaining an incomplete model with inequality restrictions on conditional median regressions. This allows us to use the estimator introduced by Manski and Tamer (2000) to analyze…

单位根检验的残差分块自助法AI 解读

A nonparametric, residual-based block bootstrap procedure is proposed in the context of testing for integrated (unit root) time series. The resampling procedure is based on weak assumptions on the dependence structure of the stationary process driving the random walk and successfully generates unit root integrated pseudo-series retaining the important characteristics of the data. It is more general than previous bootstrap approaches to the unit root problem in that it allows…

一类家庭生产模型的识别与估计AI 解读

Abstract We consider a class of household production models characterized by a dichotomy property. In these models the amount of time spent on household production does not depend on the household utility function, conditional on household members having a paid job. We analyse the (non‐parametric) identifiability of the production function and the so‐called jointness function (a function describing which part of household production time is counted as pure leisure). It is…

自回归模型随机系数的有效检测AI 解读

The problem of detecting randomness in the coefficients of an AR(p) model, that is, the problem of testing ordinary AR(p) dependence against the alternative of a random coefficient autoregressive [RCAR(p)] model is considered. A nonstandard LAN property is established for RCAR(p) models in the vicinity of AR(p) ones. Two main problems arise in this context. The first problem is related to the statistical model itself: Gaussian assumptions are highly unrealistic in a…

具有 GARCH 误差的非平稳 ARMA 模型中的自适应估计AI 解读

This paper considers adaptive estimation in nonstationary autoregressive moving average models with the noise sequence satisfyinga generalized autoregressive conditional heteroscedastic process. The locally asymptotic quadratic form of the log-likelihood ratio for the model is obtained. It is shown that the limit experiment is neither LAN nor LAMN, but is instead LABF. For the model with symmetric density of the rescaled error, a new efficiency criterion is established for a…