The Evaluation of Integrals of the form ∫ +∞ −∞ f ( t )exp(− t 2 ) dt : Application to Logistic-Normal Models

Logistic-normal distributions and related functions arise in a variety of statistical applications of current interest, including binary measurement-error models and the analysis of teratogenicity experiments. Analytic intractability has led to the development of numerous approximations to the desired forms, often with consequences that have not been well studied. A method is developed to compute these forms to arbitrary accuracy, and comparative calculations are made that show when the common numerical alternative, 20-point Gaussian quadrature, begins to fail. By using a simple matrix transformation, this method can be used with multiple covariate regression models of the logistic-normal form. We conducted a simulation study that compares the ability of 20-point Gaussian quadrature and our new method to obtain the maximum likelihood estimator of relative risk in the logistic-normal measurement-error model. Using standard subroutines to maximize the likelihood equations, 27 of 50 trials failed to converge with 20-point Gaussian quadrature, whereas the new method allowed convergence in all but one case.

摘要来源：OpenAlex 索引摘要（稿件版本未注明）

笔记与分组

同方向的基础文献

相关领域论文

共同方法：矩估计与似然、一般估计、极大与准极大似然

You've Got to be Efficient: Ambiguity, Misspecification and Variational Preferences

Karun Adusumilli首次提交 2026-04-07 · 修订 2026-10-13 · v7arXiv预印本

This article introduces a framework for evaluating statistical decisions under both prior ambiguity and likelihood misspecification. We begin with an ambiguity set - a frequentist model that pairs a possibly misspecified likelihood with every possible prior - and uniformly expand it by a Kullback-Leibler radius to accommodate likelihood misspecification. We show that optimal decisions under this framework are equivalent to minimax decisions with an exponentially tilted loss…

半参数与效率 GMM 与矩条件极大与准极大似然

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-01

阅读原文 ↗BibTeX ↓

共同方法：矩估计与似然、一般估计、极大与准极大似然

Sharp Asymptotic Theory of Maximum Likelihood Estimation for Gaussian Processes with an RBF Kernel

Ameer Qaqish · Sudipto Banerjee · Didong Li首次提交 2026-10-07 · 修订 2026-10-13 · v2arXiv预印本

RBF 核高斯过程最大似然估计的精确渐近理论AI 解读

Gaussian processes (GPs) are widely used across machine learning, spatial statistics, time-series analysis, optimization, Bayesian statistics, and scientific applications. A central component of a GP model is its kernel, which is typically specified through a parametric family. Among the most widely used choices is the radial basis function (RBF), also known as the squared exponential or Gaussian kernel, owing to its simple form, smoothness, and flexibility. In practice, the…

核、筛与局部多项式极大与准极大似然时间序列

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓

共同方法：矩估计与似然、一般估计、极大与准极大似然

Estimation of Rational Inattention--Additive Random Utility Models

Ruolei Zhu首次提交 2026-10-12 · 修订 2026-10-12 · v1arXiv预印本

理性疏忽—加性随机效用模型的估计AI 解读

This paper develops likelihood-based estimation for parametric rational inattention--additive random utility models. We establish consistency and asymptotic normality of the exact maximum likelihood estimator and provide conditions under which the simulated estimator shares its asymptotic distribution. To implement estimation, we develop Newton--BA, a KKT-guided hybrid of Newton--Raphson and Blahut--Arimoto (BA). The algorithm accommodates both interior and boundary…

微观 · 离散选择极大与准极大似然微观 · 结构估计

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓

共同方法：矩估计与似然、一般估计、极大与准极大似然

When Sample-Splitting Choices Matter in TMLE and AIPW: Cross-Fitting, Targeting, and Repetition

M. Ehsan Karim · Rainie Fu首次提交 2026-10-12 · 修订 2026-10-12 · v1arXiv预印本

TMLE 与 AIPW 中样本分割选择何时重要：交叉拟合、目标化与重复AI 解读

Variants of targeted maximum likelihood estimation (TMLE) and augmented inverse-probability weighting (AIPW) with machine-learning nuisance models differ in which observations train them, how bias is corrected, and whether splits are repeated. In known-truth simulations of a binary-outcome average treatment effect, eight headline estimators shared each setting's library, inner folds and truncation bound; coverage differences were judged against a prespecified…

微观 · 因果推断机器学习与 DML 极大与准极大似然

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓

共同方法：矩估计与似然、一般估计、极大与准极大似然

Identification problem and quasi-maximum likelihood estimation for matrix-variate CP-factor models

Hanzi Ye · Chun Yip Yau首次提交 2026-09-17 · 修订 2026-10-10 · v2arXiv预印本

矩阵值 CP 因子模型的识别问题与准极大似然估计AI 解读

Matrix-valued time series, arising in diverse fields such as economics, neuroscience, and recommender systems, have become increasingly prominent in modern data analysis. Among various modeling frameworks, the matrix-variate CP-factor model represents an important and widely applicable class for capturing low-rank structures in matrix time series. In this paper, we provide a unified treatment for the identification problem of CP factor models using both analytic and…

宏观 · 时间序列极大与准极大似然宏观 · 因子模型

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-01

AI 解读阅读原文 ↗BibTeX ↓

共同方法：矩估计与似然、一般估计、极大与准极大似然

Sparse estimation for the drift of high-dimensional Ornstein--Uhlenbeck processes with i.i.d. paths

Shogo Nakakita首次提交 2025-10-24 · 修订 2026-10-10 · v3arXiv预印本

高维 Ornstein–Uhlenbeck 过程漂移项的稀疏估计AI 解读

We study sparsity-regularized maximum likelihood estimation for the drift parameter of high-dimensional non-stationary Ornstein--Uhlenbeck processes given repeated measurements of i.i.d. paths. In particular, we show that Lasso and Slope estimators can achieve the minimax optimal rate of convergence. We exhibit numerical experiments for sparse estimation methods and show their performance.

宏观 · 时间序列正则化与变量选择极大与准极大似然

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓