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一般高斯过程的最优估计AI 解读

This paper proposes a novel exact maximum likelihood (ML) estimation method for general Gaussian processes, where all parameters are estimated jointly. The exact ML estimator (MLE) is consistent and asymptotically normally distributed. We prove the local asymptotic normality (LAN) property of the sequence of statistical experiments for general Gaussian processes in the sense of Le Cam, thereby enabling optimal estimation and facilitating statistical inference. The results…

基于迭代硬阈值的学习二元结果稀疏广义线性模型AI 解读

In statistics, generalized linear models (GLMs) are widely used for modeling data and can expressively capture potential nonlinear dependence of the model's outcomes on its covariates. Within the broad family of GLMs, those with binary outcomes, which include logistic and probit regressions, are motivated by common tasks such as binary classification with (possibly) non-separable data. In addition, in modern machine learning and statistics, data is often high-dimensional yet…

分类数据分析中的贝叶斯估计与正则化方法AI 解读

This paper explores Bayesian estimation for categorical data, focusing on simple yet effective models that provide a foundation for applying more advanced methods accurately and reliably in real-world applications. We begin by revisiting Bayesian estimators for the binomial distribution and investigating their properties. Next, we develop hypothesis tests for categorical data (sign test, homogeneity test, symmetry test) based on regularized maximum likelihood estimates of…

多项 Logit 与嵌套 Logit 估计的锥优化方法AI 解读

In this paper, we revisit parameter estimation for multinomial logit (MNL), nested logit (NL), and tree-nested logit (TNL) models through the framework of convex conic optimization. Traditional approaches typically solve the maximum likelihood estimation (MLE) problem using gradient-based methods, which are sensitive to step-size selection and initialization, and may therefore suffer from slow or unstable convergence. In contrast, we propose a novel estimation strategy that…

固定效应动态面板 Logit 模型的根号 n 一致条件极大似然估计AI 解读

In this paper we first propose a root-n-consistent Conditional Maximum Likelihood (CML) estimator for all the common parameters in the panel logit AR(p) model with strictly exogenous covariates and fixed effects. Our CML estimator (CMLE) converges in probability faster and is more easily computed than the kernel-weighted CMLE of Honoré and Kyriazidou (2000). Next, we propose a root-n-consistent CMLE for the coefficients of the exogenous covariates only. We also discuss new…

G-HIVE：含隐藏变量的多响应广义线性模型参数估计与近似推断AI 解读

In practice, there often exist unobserved variables, also termed hidden variables, associated with both the response and covariates. Existing works in the literature mostly focus on linear regression with hidden variables. However, when the regression model is non-linear, the presence of hidden variables leads to new challenges in parameter identification, estimation, and statistical inference. This paper studies multivariate response generalized linear models (GLMs) with…

高维混杂下的因果机器学习方法与交叉拟合AI 解读

Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is challenging due to the presence of high-dimensional confounding, which occurs when there are many confounders relative to sample size or complex relationships between continuous confounders and exposure and outcome. Doubly robust methods such as Augmented Inverse Probability Weighting (AIPW) and…

基于 HAL 的因果剂量反应曲线插件估计与逐点渐近正态性AI 解读

Estimating and obtaining reliable inference for the marginally adjusted causal dose-response curve for continuous treatments without relying on parametric assumptions is a well-known statistical challenge. Parametric models risk introducing significant bias through model misspecification, compromising the accurate representation of the underlying data and dose-response relationship. On the other hand, nonparametric models face difficulties as the dose-response curve is not…

具有马尔可夫结构的面板数据混合逻辑回归模型AI 解读

In this study, we propose a mixture logistic regression model with a Markov structure, and consider the estimation of model parameters using maximum likelihood estimation. We also provide a forward type variable selection algorithm to choose the important explanatory variables to reduce the number of parameters in the proposed model.

序贯自适应实验的有效统计估计及其对自适应设计的启示AI 解读

Adaptive experimental designs have gained popularity in clinical trials and online experiments. Unlike traditional, fixed experimental designs, adaptive designs can dynamically adjust treatment randomization probabilities and other design features in response to data accumulated sequentially during the experiment. These adaptations are useful to achieve diverse objectives, including reducing uncertainty in the estimation of causal estimands or increasing participants'…

具有 S-vine Copula 结构的近似因子模型AI 解读

We propose a novel framework for approximate factor models that integrates an S-vine copula structure to capture complex dependencies among common factors. Our estimation procedure proceeds in two steps: first, we apply principal component analysis (PCA) to extract the factors; second, we employ maximum likelihood estimation that combines kernel density estimation for the margins with an S-vine copula to model the dependence structure. Jointly fitting the S-vine copula with…

利用概率模型与治疗选择准则构建网络荟萃分析的治疗层级AI 解读

A key output of network meta-analysis (NMA) is the relative ranking of treatments; nevertheless, it has attracted substantial criticism. Existing ranking methods often lack clear interpretability and fail to adequately account for uncertainty, over-emphasizing small differences in treatment effects. We propose a novel framework to estimate treatment hierarchies in NMA using a probabilistic model, focusing on a clinically relevant treatment-choice criterion (TCC). Initially,…

高维非线性混合效应模型中的估计与变量选择AI 解读

We consider nonlinear mixed effects models including high-dimensional covariates to model individual parameters variability. The objective is to identify relevant covariates among a large set under sparsity assumption and to estimate model parameters. To face the high dimensional setting we consider a regularized estimator namely the maximum likelihood estimator penalized with the l1-penalty. We rely on the use of the eBIC model choice criteria to select an optimal reduced…

含协变量与增长主体数的广义 Bradley-Terry 配对比较模型推断AI 解读

Motivated by the home-field advantage in sports, we propose a generalized Bradley--Terry model that incorporates covariate information for paired comparisons. It has an $n$-dimensional merit parameter $\bsβ$ and a fixed-dimensional regression coefficient $\bsγ$ for covariates. When the number of subjects $n$ approaches infinity and the number of comparisons between any two subjects is fixed, we show the uniform consistency of the maximum likelihood estimator (MLE) $(\widehat{\bsβ}, \widehat{\bsγ})$…

结构方程建模中构念类型选择的误设问题AI 解读

Empirical research in many social disciplines involves constructs that are not directly observable, such as behaviors. To model them, constructs must be operationalized using their relations with indicators. Structural equation modeling (SEM) is the primary approach for this purpose. In SEM, three types of constructs are distinguished: latent variables, causal-formative constructs, and composites. To estimate the parameters of the different models, various estimators have…

分批自适应网络形成AI 解读

Networks are central to many economic and organizational applications, including workplace team formation, social platform recommendations, and classroom friendship development. In these settings, networks are modeled as graphs, with agents as nodes, agent pairs as edges, and edge weights capturing pairwise production or interaction outcomes. This paper develops an adaptive, or \textit{online}, policy that learns to form increasingly effective networks as data accumulates…

利用高维辅助表型改进基因组预测：遗传潜因子方法AI 解读

Decreasing costs and new technologies have led to an increase in the amount of data available to plant breeding programs. High-throughput phenotyping (HTP) platforms routinely generate high-dimensional datasets of secondary features that may be used to improve genomic prediction accuracy. However, integration of these data comes with challenges such as multicollinearity, parameter estimation in $p > n$ settings, and the computational complexity of many standard approaches.…

具有有序结果变量的成对数据模型AI 解读

We consider ordered logit models for directed network data that allow for flexible sender and receiver fixed effects that can vary arbitrarily across outcome categories. This structure poses a significant incidental parameter problem, particularly challenging under network sparsity or when some outcome categories are rare. We develop the first estimation method for this setting by extending tetrad-differencing conditional maximum likelihood (CML) techniques from binary…

因子增强框架下的二元响应预测AI 解读

In this paper, we propose a novel factor-augmented forecasting regression model with a binary response variable. We develop a maximum likelihood estimation method for the regression parameters and establish the asymptotic properties of the resulting estimators. Monte Carlo simulation results show that the proposed estimation method performs very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through an application to U.S. recession…

离散值时间序列的预测推断AI 解读

For discrete-valued time series, predictive inference cannot be implemented through the construction of prediction intervals to some predetermined coverage level, as this is the case for real-valued time series. To address this problem, we propose to reverse the construction principle by considering preselected sets of interest and estimating the probability that a future observation of the process falls into these sets. The accuracy of the prediction is then evaluated by…

用于因果效应不确定性量化的贝叶斯目标极大似然估计AI 解读

Robust decision making involves making decisions in the presence of uncertainty and is often used in critical domains such as healthcare, supply chains, and finance. Causality plays a crucial role in decision-making as it predicts the change in an outcome (usually a key performance indicator) due to a treatment (also called an intervention). To facilitate robust decision making using causality, this paper proposes three Bayesian approaches of the popular Targeted Maximum…

零膨胀混合中介变量的因果中介分析AI 解读

Causal mediation analysis is an important statistical tool to quantify effects transmitted by intermediate variables from a cause to an outcome. There is a gap in mediation analysis methods to handle mixture mediator data that are zero-inflated with multi-modality and atypical behaviors. We propose an innovative way to model zero-inflated mixture mediators from the perspective of finite mixture distributions to flexibly capture such mediator data. Multiple data types are…

用于双变量非高斯时间序列预测的广义自回归模型AI 解读

This paper introduces a novel approach, the bivariate generalized autoregressive (BGAR) model, for modeling and forecasting bivariate time series data. The BGAR model generalizes the bivariate vector autoregressive (VAR) models by allowing data that does not necessarily follow a normal distribution. We consider a random vector of two time series and assume each belongs to the canonical exponential family, similarly to the univariate generalized autoregressive moving average…

半参数时间序列模型的伪方差准极大似然估计AI 解读

We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional expectation is modeled through a parametric function. The proposed class of estimators is based on a Gaussian quasi-likelihood function and it relies on the specification of a parametric pseudo-variance that can contain parametric restrictions with respect to the conditional expectation. The specification of the pseudo-variance and the parametric…