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生产过程两阶段半参数模型的估计与推断AI 解读

A plethora of papers have used multi-stage estimation procedures where nonparametric estimates of productive efficiency are obtained in the first stage and then regressed on environmental variables in a subsequent stage in attempts to account for exogenous factors that might affect firms’ performance. None of these papers have described a coherent data-generating process (DGP). Moreover, conventional approaches to inference employed in these papers are invalid due to…

欧元区的国际联系：全球 VAR 分析AI 解读

Abstract This paper presents a quarterly global model combining individual country vector error‐correcting models in which the domestic variables are related to the country‐specific foreign variables. The global VAR (GVAR) model is estimated for 26 countries, the euro area being treated as a single economy, over the period 1979–2003. It advances research in this area in a number of directions. In particular, it provides a theoretical framework where the GVAR is derived as an…

食品风险评估中分析数据左删失的非参数建模AI 解读

Contaminants and natural toxicants such as mycotoxins may be present in various food items that may be considered dangerous to human health if the cumulative intake remains above the toxicologic safe references. This intake or exposure can be estimated using both consumption surveys and analytical data that record the contamination levels of food. Analytical data often present some left censorship, that is, data below some limit of detection or quantification. This article…

通过几何下界推断物种数量AI 解读

Estimating the number of species in a population from a sample of individuals is investigated in a nonparametric Poisson mixture model. A sequence of lower bounds to the odds that a species is unseen in the sample are proposed from a geometric perspective. A lower bound and its representing mixing distribution can be computed by linear programming with guaranteed convergence. These lower bounds can be estimated by the maximum likelihood method and used to construct lower…

用 Fourier 方法估计回归的中心子空间与中心均值子空间AI 解读

In regression with a high-dimensional predictor vector, it is important to estimate the central and central mean subspaces that preserve sufficient information about the response and the mean response. Using the Fourier transform, we have derived the candidate matrices whose column spaces recover the central and central mean subspaces exhaustively. Under the normality assumption of the predictors, explicit estimates of the central and central mean subspaces are derived.…

相关结果线性模型的泛函式分布诊断AI 解读

In this paper we present an easy-to-implement graphical distribution diagnostic for linear models with correlated errors. Houseman et al. (2004) constructed quantile–quantile plots for the marginal residuals of such models, suitably transformed. We extend the pointwise asymptotic theory to address the global stochastic behaviour of the corresponding empirical cumulative distribution function, and describe a simulation technique that serves as a computationally efficient…

作者回复：局部 Rademacher 复杂度与风险最小化中的 oracle 不等式AI 解读

I would like to thank the discussants for a number of deep and interesting comments and for their inspiring work on the subject over the years.I will not be able to address all the issues raised in the discussion; I will concentrate just on several of them. Local complexities and excess risk bounds.The first question is about possible ways to define distribution-and data-dependent complexities (such as local Rademacher complexities).The approach taken in my paper is based on…

ABSTRACT We apply a new bootstrap statistical technique to examine the performance of the U.S. open‐end, domestic equity mutual fund industry over the 1975 to 2002 period. A bootstrap approach is necessary because the cross section of mutual fund alphas has a complex nonnormal distribution due to heterogeneous risk‐taking by funds as well as nonnormalities in individual fund alpha distributions. Our bootstrap approach uncovers findings that differ from many past studies.…

利用投影的回归模型一致诊断检验AI 解读

This paper proposes a consistent test for the goodness-of-fit of parametric regression models that overcomes two important problems of the existing tests, namely, the poor empirical power and size performance of the tests due to the curse of dimensionality and the subjective choice of parameters such as bandwidths, kernels, and integrating measures. We overcome these problems by using a residual marked empirical process based on projections (RMPP). We study the asymptotic…

用于时间序列预测的函数型小波—核方法AI 解读

Summary We consider the prediction problem of a time series on a whole time interval in terms of its past. The approach that we adopt is based on functional kernel nonparametric regression estimation techniques where observations are discrete recordings of segments of an underlying stochastic process considered as curves. These curves are assumed to lie within the space of continuous functions, and the discretized time series data set consists of a relatively small, compared…

VAR 模型中协整秩检验与选择的自助法算法AI 解读

In this paper a bootstrap algorithm for a reduced rank vector autoregressive model with a restricted linear trend and independent, identically distributed errors is analyzed. For testing the cointegration rank, the asymptotic distribution under the hypothesis is the same as for the usual likelihood ratio test, so that the bootstrap is consistent. It is furthermore shown that a bootstrap procedure for determining the rank is asymptotically consistent in the sense that the…

短时段内协整关系失效的检验AI 解读

This article introduces tests for cointegration breakdown that may occur over a relatively short time period, such as at the end of the sample. The breakdown may be due to a shift in the cointegrating vector or due to a shift in the errors from being I(0) to being I(1). Tests are introduced based on the postbreakdown sum of squared residuals and sum of squared reverse partial sums of residuals. Critical values are provided using a subsampling method. Asymptotic results take…

随机组大小的组序贯试验置信区间及生存分析应用AI 解读

A new ordering scheme for defining quantiles of the multivariate distribution of a stopping time and a stopped stochastic process is introduced. This ordering scheme is used in conjunction with resampling methods to construct confidence intervals for a population mean following a group sequential test with random group sizes, and for the regression parameter of a proportional hazards model following a time-sequential clinical trial with censored survival data. It is shown…

评估函数型数据的有限维性AI 解读

Summary If a problem in functional data analysis is low dimensional then the methodology for its solution can often be reduced to relatively conventional techniques in multivariate analysis. Hence, there is intrinsic interest in assessing the finite dimensionality of functional data. We show that this problem has several unique features. From some viewpoints the problem is trivial, in the sense that continuously distributed functional data which are exactly finite…

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鞅差假设的广义谱检验AI 解读

This article proposes a test for the Martingale Difference Hypothesis (MDH) using depen- dence measures related to the characteristic function. The MDH typically has been tested using the sample autocorrelations or in the spectral domain using the periodogram. Tests based on these statistics are inconsistent against uncorrelated non-martingales processes. Here, we gen- eralize the spectral test of Durlauf (1991) for testing the MDH taking into account linear and nonlinear…

含干扰参数的非标准 M 估计之 m-out-of-n 自助法AI 解读

Nonstandard M-estimation, with nuisance parameters consistently estimated in the criterion function, often yields M-estimators converging weakly at rates different from n1/2 with weak limits that are typically non-Gaussian. The complicated asymptotics involved makes distributional estimation of the M-estimators analytically prohibitive. We show that the problem is resolved by m out of n bootstrapping under very general conditions, which provides a universal and convenient…

自然实验中的随机化推断AI 解读

Since the 2000 U.S. Presidential election, social scientists have rediscovered a long tradition of research examining the effects of ballot format on voting. Using a new dataset collected by The New York Times, we investigate the causal effect of being listed on the first ballot page in the 2003 California gubernatorial recall election. California law mandates a unique randomization procedure of ballot order that, when appropriately modeled, can be used to approximate a…

基于多元 MM 估计及快速稳健自助法的主成分分析AI 解读

We consider robust principal components analysis (PCA) based on multivariate MM estimators. We first study the robustness and efficiency of these estimators, particularly in terms of eigenvalues and eigenvectors. We then focus on inference procedures based on a fast and robust bootstrap for MM estimators. This method is an alternative to the approach based on the asymptotic distribution of the estimators and can also be used to assess the stability of the principal…

纹理合成与随机场的非参数重抽样AI 解读

This paper introduces a nonparametric algorithm for bootstrapping a stationary random field and proves certain consistency properties of the algorithm for the case of mixing random fields. The motivation for this paper comes from relating a heuristic texture synthesis algorithm popular in computer vision to general nonparametric bootstrapping of stationary random fields. We give a formal resampling scheme for the heuristic texture algorithm and prove that it produces a…

嵌套误差回归模型中均方预测误差的非参数估计AI 解读

Nested-error regression models are widely used for analyzing clustered data. For example, they are often applied to two-stage sample surveys, and in biology and econometrics. Prediction is usually the main goal of such analyses, and mean-squared prediction error is the main way in which prediction performance is measured. In this paper we suggest a new approach to estimating mean-squared prediction error. We introduce a matched-moment, double-bootstrap algorithm, enabling…

随机设计下空间回归模型的重抽样方法AI 解读

In this paper we consider the problem of bootstrapping a class of spatial regression models when the sampling sites are generated by a (possibly nonuniform) stochastic design and are irregularly spaced. It is shown that the natural extension of the existing block bootstrap methods for grid spatial data does not work for irregularly spaced spatial data under nonuniform stochastic designs. A variant of the blocking mechanism is proposed. It is shown that the proposed block…

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