论文检索

灵活的面板随机生产前沿模型AI 解读

Summary We propose a flexible stochastic production frontier model with fixed effects for the panel data in which the semiparametric frontier is additive with bivariate interactions. To avoid potential misspecification and/or “wrong skew problem” due to distributional assumptions, we model the conditional mean of the inefficiency to depend on environmental variables and to be known up to a vector of parameters. We propose a difference‐based estimator for parameters…

核插值的泛化表现不佳AI 解读

Summary One of the most interesting problems in the recent renaissance of the studies in kernel regression might be whether kernel interpolation can generalize well, since it may help us understand the ‘benign overfitting phenomenon’ reported in the literature on deep networks. In this paper, under mild conditions, we show that, for any ε>0, the generalization error of kernel interpolation is lower bounded by Ω(n−ε). In other words, the kernel interpolation generalizes…

因果函数的核方法：剂量、异质性与增量响应曲线AI 解读

Abstract We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous and incremental response curves. The treatment and covariates may be discrete or continuous in general spaces. Because of a decomposition property specific to the reproducing kernel Hilbert space, our estimators have simple closed-form solutions. We prove uniform consistency with finite sample rates via an original analysis of generalized kernel…

基于交叉验证的点过程统计理论AI 解读

Abstract Motivated by the general ability of cross-validation to reduce overfitting and mean square error, we develop a cross-validation-based statistical theory for general point processes. It is based on the combination of two novel concepts for general point processes: cross-validation and prediction errors. Our cross-validation approach uses thinning to split a point process/pattern into pairs of training and validation sets, while our prediction errors measure…

对非平稳性稳健的预白化长期方差估计AI 解读

We introduce a nonparametric nonlinear VAR prewhitened long-run variance (LRV) estimator for the construction of standard errors robust to autocorrelation and heteroskedasticity that can be used for hypothesis testing in a variety of contexts including the linear regression model. Existing methods either are theoretically valid only under stationarity and have poor finite-sample properties under nonstationarity (i.e., fixed-b methods), or are theoretically valid under the…

洪永淼、Oliver Linton、孙佳静与朱美婷对机器学习概率与统计讨论会的评论AI 解读

This paper proposes a neural network-based approach for automating offline change-point detection. The authors show that cumulative sum (CUSUM) and generalized CUSUM are a special case of their neural network class. They emphasize misclassification error rates and their theoretical contribution is to establish some elegant results for these under i.i.d. unit variance Gaussian data with a possible change in mean. Their theoretical results outline the conditions on the…

高维稀疏卷积秩回归AI 解读

Wang et al. studied the high-dimensional sparse penalized rank regression and established its nice theoretical properties. Compared with the least squares, rank regression can have a substantial gain in estimation efficiency while maintaining a minimal relative efficiency of 86.4%. However, the computation of penalized rank regression can be very challenging for high-dimensional data, due to the highly nonsmooth rank regression loss. In this work we view the rank regression…

贝叶斯条件变换模型AI 解读

Recent developments in statistical regression methodology shift away from pure mean regression toward distributional regression models. One important strand thereof is that of conditional transformation models (CTMs). CTMs infer the entire conditional distribution directly by applying a transformation function to the response conditionally on a set of covariates toward a simple log-concave reference distribution. Thereby, CTMs allow not only variance, kurtosis or skewness…

高维阈值参数的显著性检验：个体化最小临床重要差异应用AI 解读

This work is motivated by learning the individualized minimal clinically important difference, a vital concept to assess clinical importance in various biomedical studies. We formulate the scientific question into a high-dimensional statistical problem where the parameter of interest lies in an individualized linear threshold. The goal is to develop a hypothesis testing procedure for the significance of a single element in this parameter as well as of a linear combination of…

广义可加模型的在线平滑回拟合AI 解读

We propose an online smoothing backfitting method for generalized additive models coupled with local linear estimation. The idea can be extended to general nonlinear optimization problems. The strategy is to use an appropriate-order expansion to approximate the nonlinear equations and store the coefficients as sufficient statistics which can be updated in an online manner by the dynamic candidate bandwidth method. We investigate the statistical and algorithmic convergences…

不规则位置与跨位置非平稳下的半参数动态函数系数时空自回归模型AI 解读

Nonlinear dynamic modeling of spatio-temporal data is often a challenge, especially due to irregularly observed locations and location-wide nonstationarity.In this article we propose a semiparametric family of Dynamic Functional-coefficient Autoregressive Spatio-Temporal (DyFAST) models to address the difficulties.We specify the autoregressive smoothing coefficients depending dynamically on both a concerned regime and location so that the models can characterize not only the…

含终止事件纵向数据的二元时变系数模型核估计AI 解读

We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model…

具有理论保证的函数型张量奇异值分解AI 解读

This paper introduces the functional tensor singular value decomposition (FTSVD), a novel dimension reduction framework for tensors with one functional mode and several tabular modes. The problem is motivated by high-order longitudinal data analysis. Our model assumes the observed data to be a random realization of an approximate CP low-rank functional tensor measured on a discrete time grid. Incorporating tensor algebra and the theory of Reproducing Kernel Hilbert Space…

局部自归一化多变点检验的一般构造框架AI 解读

–We propose a general framework to construct self-normalized multiple-change-point tests with time series data. The only building block is a user-specified single-change-detecting statistic, which covers a large class of popular methods, including the cumulative sum process, outlier-robust rank statistics, and order statistics. The proposed test statistic does not require robust and consistent estimation of nuisance parameters, selection of bandwidth parameters, nor…

以部分函数型自回归建模函数时间序列与混合类型预测变量AI 解读

In many business and economics studies, researchers have sought to measure the dynamic dependence of curves with high-dimensional mixed-type predictors. We propose a partially functional autoregressive model (pFAR) where the serial dependence of curves is controlled by coefficient operators that are defined on a two-dimensional surface, and the individual and group effects of mixed-type predictors are estimated with a two-layer regularization. We develop an efficient…

套利特征的动态同类群组AI 解读

We propose an asset pricing factor model constructed with semiparametric characteristics-based mispricing and factor loading functions. We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-splines coefficients, with the strengthened power…

异质性因果效应估计的极小极大收敛率AI 解读

Estimation of heterogeneous causal effects - i.e., how effects of policies and treatments vary across subjects - is a fundamental task in causal inference. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years, but questions surrounding optimality have remained largely unanswered. In particular, a minimax theory of optimality has yet to be developed, with the minimax rate of convergence and construction of rate-optimal…

部分线性模型中条件分位数过程的推断及失业福利影响应用AI 解读

Abstract We propose methods to estimate and make inferences on conditional quantile processes for models with both nonparametric and (locally or globally) linear components. We derive their asymptotic properties, optimal bandwidths, and uniform confidence bands over quantiles allowing for robust bias correction. Our framework covers the sharp regression discontinuity design, which is used to study the effects of unemployment insurance benefits extensions, focusing on…

Newey–West在一阶核中是否最优？AI 解读

Newey–West (1987) standard errors are the dominant standard errors used for heteroskedasticity and autocorrelation robust (HAR) inference in time series regression. The Newey–West estimator uses the Bartlett kernel, which is a first-order kernel, meaning that its characteristic exponent, q, is equal to 1, where q is defined as the largest value of r for which the quantity k[r](0)=limt→0|t|−r(k(0)−k(t)) is defined and finite. This raises the apparently uninvestigated question…

无向二元关系数据的核密度估计AI 解读

We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables defined for all n≡defN2 unordered pairs of agents/nodes in a weighted network of order N). These random variables satisfy a local dependence property: any random variables in the network that share one or two indices may be dependent, while those sharing no indices in common are independent. In this setting, we show that density functions may be estimated by…

简单条件下 Metropolis 调整 Langevin 算法的几何收敛AI 解读

Summary While the Metropolis-adjusted Langevin algorithm is a popular and widely used Markov chain Monte Carlo method, very few papers derive conditions that ensure its convergence. In particular, to the authors’ knowledge, assumptions that are both easy to verify and guarantee geometric convergence, are still missing. In this work, we establish V-uniformly geometric convergence for the Metropolis-adjusted Langevin algorithm under mild assumptions about the target…

基于最大均值差异的通用稳健回归AI 解读

Abstract Many modern datasets are collected automatically and are thus easily contaminated by outliers. This has led to a renewed interest in robust estimation, including new notions of robustness such as robustness to adversarial contamination of the data. However, most robust estimation methods are designed for a specific model. Notably, many methods were proposed recently to obtain robust estimators in linear models, or generalized linear models, and a few were developed…

重新审视免带宽推断：样本分割方法AI 解读

Abstract The bandwidth-free tests for a multi-dimensional parameter have attracted considerable attention in econometrics and statistics literature. These tests can be conveniently implemented due to their tuning-parameter free nature and possess more accurate size as compared to the traditional heteroskedasticity and autocorrelation consistent-based approaches. However, when sample size is small/medium, these bandwidth-free tests exhibit large size distortion when both the…

Huk、Pacchiardi、Dutta 与 Steel 对《鞅后验分布》的讨论AI 解读

We congratulate the authors for this thought-provoking paper. The recursive definition of the predictive distributions through a bivariate copula (Section 4) depends on a hyperparameter ρ, which the authors tune by minimising the prequential log-score −∑i=1nlogpi−1(yi) (Section 4.5.2). The prequential framework nicely connects with the predictive resampling approach used later. However, other strictly proper scoring rules (Gneiting & Raftery, 2007) could be used in place of…