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分位数相关与分位数自回归建模AI 解读

In this article, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the Box–Jenkins three-stage procedure (model identification, model parameter estimation, and model diagnostic checking) from…

丰富数据环境下经济变量的分布预测AI 解读

This article investigates the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable. The baseline time series model is a semiparametric specification based on the quantile autoregressive (QAR) model that assumes that the quantiles depend on the lagged values of the variable. We then augment the time series model with macroeconomic information from a large dataset by including principal components or a subset…

调整半竞争风险所致依赖删失的分位数回归AI 解读

Summary We study quantile regression when the response is an event time subject to potentially dependent censoring. We consider the semicompeting risks setting, where the time to censoring remains observable after the occurrence of the event of interest. Although such a scenario frequently arises in biomedical studies, most of current quantile regression methods for censored data are not applicable because they generally require the censoring time and the event time to be…

工具变量有效性检验AI 解读

This paper develops a specification test for instrument validity in the heterogeneous treatment effect model with a binary treatment and a discrete instrument. The strongest testable implication for instrument validity is given by the condition for nonnegativity of point‐identifiable compliers' outcome densities. Our specification test infers this testable implication using a variance‐weighted Kolmogorov–Smirnov test statistic. The test can be applied to both discrete and…

离散选择的非参数福利分析AI 解读

We consider empirical measurement of equivalent/compensating variation resulting from price-change of a discrete good using individual-level data, when there is unobserved heterogeneity in preferences.We show that for binary and unordered multinomial choice, the marginal distributions of EV/CV can be expressed as simple closed-form functionals of conditional choice-probabilities under essentially unrestricted preference-distributions.These results hold even when the…

利用现代优化求解最小平方残差分位数回归AI 解读

We address the Least Quantile of Squares (LQS) (and in particular the Least Median of Squares) regression problem using modern optimization methods. We propose a Mixed Integer Optimization (MIO) formulation of the LQS problem which allows us to find a provably global optimal solution for the LQS problem. Our MIO framework has the appealing characteristic that if we terminate the algorithm early, we obtain a solution with a guarantee on its sub-optimality. We also propose…

通过最优组合分位数信息实现高效回归估计AI 解读

We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As…

条件平均处理效应的估计AI 解读

We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE parameter is nonparametrically…

计算机实验敏感性分析中的变换与不变性AI 解读

Summary Monotonic transformations are widely employed in statistics and data analysis. In computer experiments they are often used to gain accuracy in the estimation of global sensitivity statistics. However, one faces the question of interpreting results that are obtained on the transformed data back on the original data. The situation is even more complex in computer experiments, because transformations alter the model input–output mapping and distort the estimators. This…

两步广义矩估计框架下的固定平滑渐近理论AI 解读

This paper develops the fixed-smoothing asymptotics in a two-step generalized method of moments (GMM) framework. Under this type of asymptotics, the weighting matrix in the second-step GMM criterion function converges weakly to a random matrix and the two-step GMM estimator is asymptotically mixed normal. Nevertheless, the Wald statistic, the GMM criterion function statistic, and the Lagrange multiplier statistic remain asymptotically pivotal. It is shown that critical…

研究特定参数固定且未知时的元分析AI 解读

Meta-analysis is a valuable tool for combining information from independent studies. However, most common meta-analysis techniques rely on distributional assumptions that are difficult, if not impossible, to verify. For instance, in the commonly used fixed-effects and random-effects models, we take for granted that the underlying study-level parameters are either exactly the same across individual studies or that they are realizations of a random sample from a population,…

用于估计风险价值的变系数期望分位数模型AI 解读

This article develops a nonparametric varying-coefficient approach for modeling the expectile-based value at risk (EVaR). EVaR has an advantage over the conventional quantile-based VaR (QVaR) of being more sensitive to the magnitude of extreme losses. EVaR can also be used for calculating QVaR and expected shortfall (ES) by exploiting the one-to-one mapping from expectiles to quantiles, and the relationship between VaR and ES. Previous studies on conditional EVaR estimation…

非参数贝叶斯程序中的 Bernstein–von Mises 现象AI 解读

We continue the investigation of Bernstein–von Mises theorems for nonparametric Bayes procedures from [Ann. Statist. 41 (2013) 1999–2028]. We introduce multiscale spaces on which nonparametric priors and posteriors are naturally defined, and prove Bernstein–von Mises theorems for a variety of priors in the setting of Gaussian nonparametric regression and in the i.i.d. sampling model. From these results we deduce several applications where posterior-based inference coincides…

二元生存函数的自洽非参数最大似然估计AI 解读

As usually formulated the nonparametric likelihood for the bivariate survivor function is over-parameterized, resulting in uniqueness problems for the corresponding nonparametric maximum likelihood estimator. Here the estimation problem is redefined to include parameters for marginal hazard rates, and for double failure hazard rates only at informative uncensored failure time grid points where there is pertinent empirical information. Double failure hazard rates at other…

面向处理效应估计的模型选择AI 解读

Summary Researchers often believe that a treatment's effect on a response may be heterogeneous with respect to certain baseline covariates. This is an important premise of personalized medicine. Several methods for estimating heterogeneous treatment effects have been proposed. However, little attention has been given to the problem of choosing between estimators of treatment effects. Models that best estimate the regression function may not be best for estimating the effect…

单调性约束下的非参数与半参数回归：估计与预测AI 解读

This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang (2001). Asymptotic properties of our proposed estimators and forecasts are established. Monte Carlo simulation is conducted to show their finite sample performance. An application to predicting equity premium is taken for illustration. We introduce a new forecasting evaluation criterion based on the second…

长记忆时间序列谱密度与谱分布的固定b渐近推断AI 解读

This paper studies taper-based estimates of the spectral density utilizing a fixed bandwidth ratio asymptotic framework, and makes several theoretical contributions: (i) we treat multiple frequencies jointly, (ii) we allow for long-range dependence or anti-persistence at differing frequencies, (iii) we allow for tapers that are only piecewise smooth or discontinuous, including flat-top and truncation tapers, (iv) we study higher-order accuracy through the limit…

Semiparametric quantile regression is employed to flexibly estimate sales response for frequently purchased consumer goods. Using retail store-level data, we compare the performance of models with and without monotonic smoothing for fit and prediction accuracy. We find that (a) flexible models with monotonicity constraints imposed on price effects dominate both in-sample and out-of-sample comparisons while being robust even at the boundaries of the price distribution when…

用于充分降维的自适应复合分位数方法AI 解读

Sufficient dimension reduction [J. Amer. Statist. Assoc. 86 (1991) 316–342] has long been a prominent issue in multivariate nonparametric regression analysis. To uncover the central dimension reduction space, we propose in this paper an adaptive composite quantile approach. Compared to existing methods, (1) it requires minimal assumptions and is capable of revealing all dimension reduction directions; (2) it is robust against outliers and (3) it is structure-adaptive, thus…

基于最小描述长度原则的分段分位数回归模型选择AI 解读

This article proposes new model-fitting techniques for quantiles of an observed data sequence, including methods for data segmentation and variable selection. The main contribution, however, is in providing a means to perform these two tasks simultaneously. This is achieved by matching the data with the best-fitting piecewise quantile regression model, where the fit is determined by a penalization derived from the minimum description length principle. The resulting…

鞅差相关及其在高维变量筛选中的应用AI 解读

In this article, we propose a new metric, the so-called martingale difference correlation, to measure the departure of conditional mean independence between a scalar response variable V and a vector predictor variable U. Our metric is a natural extension of distance correlation proposed by Székely, Rizzo, and Bahirov, which is used to measure the dependence between V and U. The martingale difference correlation and its empirical counterpart inherit a number of desirable…

稳定分布的精确置信集合与拟合优度方法AI 解读

Usual inference methods for stable distributions are typically based on limit distributions. But asymptotic approximations can easily be unreliable in such cases, for standard regularity conditions may not apply or may hold only weakly. This paper proposes finite-sample tests and confidence sets for tail thickness and asymmetry parameters (α and β) of stable distributions. The confidence sets are built by inverting exact goodness-of-fit tests for hypotheses which assign…

无限方差变量回归模型中决定系数的性质AI 解读

We examine the asymptotic properties of the coefficient of determination, R2, in models with α-stable random variables. If the regressor and error term share the same index of stability α<2, we show that the R2 statistic does not converge to a constant but has a nondegenerate distribution on the entire [0,1] interval. We provide closed-form expressions for the cumulative distribution function and probability density function of this limit random variable, and we show that…

金融时间序列的极端分位数跟踪AI 解读

Time series of financial asset values exhibit well-known statistical features such as heavy tails and volatility clustering. We propose a nonparametric extension of the classical Peaks-Over-Threshold method from extreme value theory to fit the time varying volatility in situations where the stationarity assumption may be violated by erratic changes of regime, say. As a result, we provide a method for estimating conditional risk measures applicable to both stationary and…