论文检索

Isadora Antoniano Villalobos 对《鞅后验分布》的讨论AI 解读

I thank the authors for their refreshing ideas, providing many insights and intuitions on the process of statistical learning. For years, Bayesians have considered two levels of uncertainty, the likelihood and the prior. By shifting the focus to the predictive distribution as a single expression of uncertainty about a future observation (given data), the sequential nature of statistical learning is made explicit, together with some of its limitations. If each observation…

Retire：高维稳健期望分位数回归AI 解读

High-dimensional data can often display heterogeneity due to heteroscedastic variance or inhomogeneous covariate effects. Penalized quantile and expectile regression methods offer useful tools to detect heteroscedasticity in high-dimensional data. The former is computationally challenging due to the non-smooth nature of the check loss, and the latter is sensitive to heavy-tailed error distributions. In this paper, we propose and study (penalized) robust expectile regression…

可分度量空间中多个分布的相等性检验：最大均值差异方法AI 解读

A new test for equal distributions of several high-dimensional samples in separable metric spaces, with its test statistic constructed based on maximum mean discrepancy, is proposed and studied. The asymptotic null and alternative distributions of the test statistic are established under some mild conditions. The new test is implemented via a three-cumulant matched chi-square approximation with the associated approximation parameters consistently estimated from the data. A…

超高维投资组合选择的资产拆分算法及其理论性质AI 解读

The presence of a huge number of assets poses challenges to classical portfolio selection algorithms. Constrained l1 minimization approaches have been proposed to directly estimate effective parameters in the optimal portfolio. Linear programming method and alternating direction method of multiplier (ADMM) algorithm is used to solve the corresponding minimization problems. However, these two algorithms may fail due to the limitations of computing time and computing memory…

线性时变系数模型的筛自助法推断AI 解读

We propose a sieve bootstrap framework to conduct pointwise and simultaneous inference for time-varying coefficient regression models based on a local linear estimator. The asymptotic validity of the sieve bootstrap in the presence of autocorrelation is established. The bootstrap automatically produces a consistent estimate of nuisance parameters, both at the interior and boundary points. In addition, we develop a bootstrap-based test for parameter constancy and examine its…

刻画精神障碍决策行为的半参数逆强化学习方法AI 解读

Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability. Motivated by the probabilistic reward task (PRT) experiment in the EMBARC study, we propose a semiparametric inverse reinforcement learning (RL) approach to…

零频率附近的谱密度估计AI 解读

Estimating the spectral density function f(w) for some w∈[−π,π] has been traditionally performed by kernel smoothing the periodogram and related techniques. Kernel smoothing is tantamount to local averaging, that is, approximating f(w) by a constant over a window of small width. Although f(w) is uniformly continuous and periodic with period 2π, in this article we recognize the fact that w = 0 effectively acts as a boundary point in the underlying kernel smoothing problem,…

快速且数值稳定的粒子在线加性平滑：AdaSmooth 算法AI 解读

We present a novel sequential Monte Carlo approach to online smoothing of additive functionals in a very general class of path-space models. Hitherto, the solutions proposed in the literature suffer from either long-term numerical instability due to particle-path degeneracy or, in the case that degeneracy is remedied by particle approximation of the so-called backward kernel, high computational demands. In order to balance optimally computational speed against numerical…

高维动态主成分分析AI 解读

Principal component analysis is a versatile tool to reduce dimensionality which has wide applications in statistics and machine learning. It is particularly useful for modeling data in high-dimensional scenarios where the number of variables p is comparable to, or much larger than the sample size n. Despite an extensive literature on this topic, researchers have focused on modeling static principal eigenvectors, which are not suitable for stochastic processes that are…

重复标记点过程中的标记与点依赖：偏差校正及检验AI 解读

Mark-point dependence plays a critical role in research problems that can be fitted into the general framework of marked point processes. In this work, we focus on adjusting for mark-point dependence when estimating the mean and covariance functions of the mark process, given independent replicates of the marked point process. We assume that the mark process is a Gaussian process and the point process is a log-Gaussian Cox process, where the mark-point dependence is…

基于惩罚 Tobit 似然的高维删失回归AI 解读

High-dimensional regression and regression with a left-censored response are each well-studied topics. In spite of this, few methods have been proposed which deal with both of these complications simultaneously. The Tobit model—long the standard method for censored regression in economics—has not been adapted for high-dimensional regression at all. To fill this gap and bring up-to-date techniques from high-dimensional statistics to the field of high-dimensional left-censored…

多重均衡下不完全信息序贯博弈的识别与估计AI 解读

This paper discusses the identification and estimation of game-theoretic models, mainly focusing on sequential games of incomplete information. In most empirical games, researchers cannot observe the exact order of actions played in the game and rely on the assumption of simultaneous actions. My structural modeling generalizes an empirical game to encompass simultaneous and sequential actions as special cases. I specify a sequential game allowing for multiple players in each…

基于汇总表数据的免调参非参数密度估计AI 解读

Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns. Motivated by this practical feature, we propose a novel nonparametric density estimation method from tabulated summary data based on maximum entropy and prove its strong uniform consistency. Unlike existing kernel-based estimators, our estimator is free from tuning parameters and admits a closed-form density that is convenient for post-estimation…

神经网络资产定价中的显著性检验AI 解读

This study proposes a novel hypothesis test for evaluating the statistical significance of input variables in multi-layer perceptron (MLP) regression models. Theoretical foundations are established through consistency results and estimation rate analysis using the sieves method. To validate the test’s performance in complex and realistic settings, an extensive Monte Carlo simulation is conducted. Results of the simulation reveal that the test has a high power and low rate of…

含零无效率质量点的随机前沿惩罚筛估计AI 解读

Summary Stochastic frontier models for cross‐sectional data typically assume that the one‐sided distribution of firm‐level inefficiency is continuous. However, it may be reasonable to hypothesize that inefficiency is continuous except for a discrete mass at zero capturing fully efficient firms (zero‐inefficiency). We propose a sieve‐type density estimator for such a mixture distribution in a nonparametric stochastic frontier setting under a unimodality‐at‐zero assumption.…

非参数工具变量模型中的自适应速率最优假设检验AI 解读

We propose a new adaptive hypothesis test for inequality (e.g., monotonicity, convexity) and equality (e.g., parametric, semiparametric) restrictions on a structural function in a nonparametric instrumental variables (NPIV) model. Our test statistic is based on a modified leave‐one‐out sample analog of a quadratic distance between the restricted and unrestricted sieve two‐stage least squares estimators. We provide computationally simple, data‐driven choices of sieve tuning…

模糊断点回归设计中的偏差感知推断AI 解读

We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. Our CSs are based on local linear regression, and are bias‐aware, in the sense that they take possible bias explicitly into account. Their construction shares similarities with that of Anderson–Rubin CSs in exactly identified instrumental variable models, and thereby avoids issues with “delta method” approximations that underlie most commonly used existing inference methods for…

利用两个次序统计量进行反卷积AI 解读

Economic data are often contaminated by measurement errors and truncated by ranking. This paper shows that the classical measurement error model with independent and additive measurement errors is identified nonparametrically using only two order statistics of repeated measurements. The identification result confirms a hypothesis by Athey and Haile (2002) for a symmetric ascending auction model with unobserved heterogeneity. Extensions allow for heterogeneous measurement…

平坦极限下的高斯过程回归AI 解读

Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regression. Most of the theoretical work on GP regression has focused on a large-n asymptotics, characterising the behaviour of GP regression as the amount of data increases. Fixed-sample analysis is much more difficult outside of simple cases, such as locations on a regular grid. In this work, we perform a…

高维数据的时变预测组合AI 解读

In this paper, we propose a new nonparametric estimator of time-varying forecast combination weights. When the number of individual forecasts is small, we study the asymptotic properties of the local linear estimator. When the number of candidate forecasts exceeds or diverges with the sample size, we consider penalized local linear estimation with the group SCAD penalty. We show that the estimator exhibits the oracle property and correctly selects relevant forecasts with…

CRPS 学习：按时间与分位数自适应组合预测AI 解读

Combination and aggregation techniques can significantly improve forecast accuracy. This also holds for probabilistic forecasting methods where predictive distributions are combined. There are several time-varying and adaptive weighting schemes such as Bayesian model averaging (BMA). However, the quality of different forecasts may vary not only over time but also within the distribution. For example, some distribution forecasts may be more accurate in the center of the…

局部平稳过程非参数估计的自适应方法AI 解读

Two adaptive bandwidth selection methods for minimizing the mean squared error of nonparametric estimators in locally stationary processes are proposed. We investigate a cross-validation approach and a method based on contrast minimization and derive asymptotic properties of both methods. The results are applicable for different statistics under a general setting of local stationarity including nonlinear processes. At the same time, we deepen the general framework for local…

非线性协整回归的最优带宽选择AI 解读

We study optimal bandwidth selection in nonparametric cointegrating regression where the regressor is a stochastic trend process driven by short or long memory innovations. Unlike stationary regression, the optimal bandwidth is found to be a random sequence which depends on the sojourn time of the process. All random sequences $h_{n}$ that lie within a wide band of rates as the sample size $n\rightarrow \infty $ have the property that local level and local linear kernel…

黎曼流形上空间点过程强度函数的非参数估计AI 解读

Summary This paper is concerned with nonparametric estimation of the intensity function of a point process on a Riemannian manifold. It provides a first-order asymptotic analysis of the proposed kernel estimator for Poisson processes, supplemented by empirical work to probe the behaviour in finite samples and under other generative regimes. The investigation highlights the scope for finite-sample improvements by allowing the bandwidth to adapt to local curvature.