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用于域泛化的域特定非参数回归AI 解读

We propose a domain-specific regression approach for domain generalization, taking into account possible heterogeneity among the datasets from different sources. In the proposed model, the domain-specific features are characterized through linear functionals of the marginal source distributions. The predictors are combined with the domain-specific linear functionals as inputs in the model. Using the source data sets, we estimate the domain-index function and the regression…

通信高效的分布式统计推断及其隐私保护改进AI 解读

In the modern era of big data, the vast amount of available data has brought more ways to analyze important economic and financial issues. For example, predicting the probability of individual default has become more accurate, as the number of defaulted individuals has increased year-on-year with the increase in data volume, leading to a more detailed characterization of the defaulted population. However, it presents new challenges and one of them is that all samples are…

利用无标签数据的半参数 ROC 曲线估计AI 解读

The receiver operating characteristic (ROC) curve is a widely used tool in various fields, including economics, medicine, and machine learning, for evaluating classification performance and comparing treatment effect. The absence of clear and readily labels is a frequent phenomenon in estimating ROC owing to various reasons like labeling cost, time constraints, data privacy and information asymmetry. Traditional supervised estimators commonly rely solely on labeled data,…

半监督框架下的一般 M 估计理论AI 解读

We study a class of general M-estimators in the semi-supervised setting, wherein the data are typically a combination of a relatively small labeled dataset and large amounts of unlabeled data. A new estimator, which efficiently uses the useful information contained in the unlabeled data, is proposed via a projection technique. We prove consistency and asymptotic normality, and provide an inference procedure based on K-fold cross-validation. The optimal weights are derived to…

从条件分位数回归到边际分位数估计：缺失数据与因果推断应用AI 解读

It is well known that information on the conditional distribution of an outcome variable given covariates can be used to obtain an enhanced estimate of the marginal outcome distribution. This can be done easily by integrating out the marginal covariate distribution from the conditional outcome distribution. However, to date, no analogy has been established between marginal quantile and conditional quantile regression. This article provides a link between them. We propose two…

DDAC-SpAM：特征划分与去相关的高维稀疏加性模型分布式算法AI 解读

–Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose a new algorithm, DDAC-SpAM, which divides the features under a high-dimensional sparse additive model. Our approach involves three steps: divide, decorrelate, and conquer. The decorrelation operation enables each local estimator to recover the sparsity pattern for each additive component without…

基于已实现GARCH–Itô模型的波动率分析AI 解读

This paper introduces a unified approach for modeling high-frequency financial data that can accommodate both the continuous-time jump–diffusion and discrete-time realized GARCH model by embedding the discrete realized GARCH structure in the continuous instantaneous volatility process. The key feature of the proposed model is that the corresponding conditional daily integrated volatility adopts an autoregressive structure, where both integrated volatility and jump variation…

用于估计最优个体化治疗规则的一致性辅助学习AI 解读

Summary We propose new concordance-assisted learning for estimating optimal individualized treatment regimes. We first introduce a type of concordance function for prescribing treatment and propose a robust rank regression method for estimating the concordance function. We then find treatment regimes, up to a threshold, to maximize the concordance function, named the prescriptive index. Finally, within the class of treatment regimes that maximize the concordance function, we…

存在缺失观测时的有效分位数回归分析AI 解读

This article examines the problem of estimation in a quantile regression model when observations are missing at random under independent and nonidentically distributed errors. We consider three approaches of handling this problem based on nonparametric inverse probability weighting, estimating equations projection, and a combination of both. An important distinguishing feature of our methods is their ability to handle missing response and/or partially missing covariates,…

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…

病例对照数据变系数密度比模型的局部经验似然推断AI 解读

In this article, we develop a varying-coefficient density-ratio model for case-control studies. The case and control samples come from two different distributions. Under the model assumption, the ratio of the two densities is related to the linear combination of covariates with varying coefficients through a known function. A special case is the exponential tilt model where the log ratio of the two densities is a linear function of covariates. We propose a local empirical…

固定效应面板部分线性可加回归模型的估计AI 解读

In this paper, we investigate the estimation problem of fixed effects panel data partially linear additive regression models. Semi‐parametric fixed effects panel data regression models are tools that are well suited to econometric analysis and the analysis of cDNA micro‐arrays. By applying a polynomial spline series approximation and a profile least‐squares procedure, we propose a semi‐parametric least‐squares dummy variables estimator (SLSDVE) for the parametric component…

异方差非参数回归中变点的小波分析AI 解读

In this paper we develop wavelet methods for detecting and estimating jumps and cusps in the mean function of a non-parametric regression model. An important characteristic of the model considered here is that it allows for conditional heteroscedastic variance, a feature frequently encountered with economic and financial data. Wavelet analysis of change-points in this model has been considered in a limited way in a recent study by Chen et al. (2008) with a focus on jumps…

线性协变量含测量误差的半参数变系数部分线性模型推断AI 解读

We study semiparametric varying-coefficient partially linear models when some linear covariates are not observed, but ancillary variables are available. Semiparametric profile least-square-based estimation procedures are developed for parametric and nonparametric components after we calibrate the error-prone covariates. Asymptotic properties of the proposed estimators are established. We also propose the profile least-square-based ratio test and Wald test to identify…

缺失数据下的估计方程推断AI 解读

There is a large and growing body of literature on estimating equation (EE) as an estimation approach. One basic property of EE that has been universally adopted in practice is that of unbiasedness, and there are deep conceptual reasons why unbiasedness is a desirable EE characteristic. This article deals with inference from EEs when data are missing at random. The investigation is motivated by the observation that direct imputation of missing data in EEs generally leads to…

条件异方差波动下利用小波平滑检测收益变化AI 解读

In this paper, we propose two estimators, an integral estimator and a discretized estimator, for the wavelet coefficient of regression functions in nonparametric regression models with heteroscedastic variance. These estimators can be used to test the jumps of the regression function. The model allows for lagged-dependent variables and other mixing regressors. The asymptotic distributions of the statistics are established, and the asymptotic critical values are analytically…

多元失效时间数据的变系数风险模型AI 解读

Statistical estimation and inference for marginal hazard models with varying coefficients for multivariate failure time data are important subjects in survival analysis. A local pseudo-partial likelihood procedure is proposed for estimating the unknown coefficient functions. A weighted average estimator is also proposed in an attempt to improve the efficiency of the estimator. The consistency and asymptotic normality of the proposed estimators are established and standard…

生存时间数据的局部部分似然估计AI 解读

This paper considers a proportional hazards model, which allows one to examine the extent to which covariates interact nonlinearly with an exposure variable, for analysis of lifetime data. A local partial-likelihood technique is proposed to estimate nonlinear interactions. Asymptotic normality of the proposed estimator is established. The baseline hazard function, the bias and the variance of the local likelihood estimator are consistently estimated. In addition, a one-step…

删失两样本问题中治疗效应的经验似然半参数推断AI 解读

To compare two samples of censored data, we propose a unified semiparametric inference for the parameter of interest when the model for one sample is parametric and that for the other is nonparametric. The parameter of interest may represent, for example, a comparison of means, or survival probabilities. The confidence interval derived from the semiparametric inference, which is based on the empirical likelihood principle, improves its counterpart constructed from the common…

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