The Performance of the Likelihood Ratio Test When the Model is Incorrect

Let the random variable $X$ have a distribution depending on a parameter $\theta \in \Theta$ . Consider the problem of testing the hypothesis $H: \Theta_0 \subseteqq \Theta$ based on a sequence of observations on $X$ . The likelihood ratio test for $H$ is constructed by selecting a model for the unknown distribution of $X$ . In this paper the asymptotic performance of the likelihood ratio test is studied when the model is incorrect, that is, when the probability distribution of $X$ is not a member of the model from which the likelihood ratio test is constructed. Exact and approximate measures of the asymptotic efficiency of the likelihood ratio test when the model is incorrect are proposed.Let the random variable $X$ have a distribution depending on a parameter $\theta \in \Theta$. Consider the problem of testing the hypothesis $H: \Theta_0 \subseteqq \Theta$ based on a sequence of observations on $X$. The likelihood ratio test for $H$ is constructed by selecting a model for the unknown distribution of $X$. In this paper the asymptotic performance of the likelihood ratio test is studied when the model is incorrect, that is, when the probability distribution of $X$ is not a member of the model from which the likelihood ratio test is constructed. Exact and approximate measures of the asymptotic efficiency of the likelihood ratio test when the model is incorrect are proposed.

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：检验与推断

Bootstrap inference in factor-augmented regressions with weak factors

Peiyun Jiang · Takashi Yamagata首次提交 2025-10-01 · 修订 2026-10-14 · v2arXiv预印本

In this paper, we develop bootstrap methods for factor-augmented regressions with potentially weak common components, allowing signal eigenvalues to diverge at heterogeneous rates $N^{α_k}$, where $0<α_k\leq1$, $k=1,\ldots,r$. The regression estimator targets a rotation of the latent parameter vector that may be data dependent, while factor estimation can induce asymptotic bias. The proposed procedure directly constructs the rotation-specific target in the bootstrap world,…

金融 · 金融因子 Bootstrap 与重抽样金融 · 收益预测

被引：暂无数据

阅读原文 ↗BibTeX ↓

共同方法：检验与推断

Improving Mendelian Randomization Analysis by Instrument Borrowing from Auxiliary Outcome Traits

Anagh Chattopadhyay · Nilanjan Chatterjee首次提交 2026-07-17 · 修订 2026-10-14 · v2arXiv预印本

Mendelian randomization (MR) infers causal effects of exposures on outcomes using genetic variants as instrumental variables, but existing methods remain vulnerable to bias and loss of power when instruments are invalid. We hypothesize that closely related outcome traits share a large overlap in their valid instruments for a given exposure, so that borrowing instruments across such traits can yield more robust MR inference. To operationalize this, we introduce a novel…

微观 · 因果推断工具变量

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-01

阅读原文 ↗BibTeX ↓

共同方法：检验与推断

Testing Conditional Distribution Equality Using Generative Models

Zekai Jin · Yi Zhang · Yun Yang · Xiaofeng Shao首次提交 2026-10-14 · 修订 2026-10-14 · v1arXiv预印本

使用生成模型检验条件分布相等AI 解读

Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning. Existing methods often lose power when the conditioning variable is high-dimensional. We propose a new test that leverages generative models to compare conditional distributions using both observed data and synthetic samples. A sample-splitting and studentization scheme is introduced to control estimation error. Under the null, the test…

微观 · 因果推断高维与机器学习核、筛与局部多项式

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓

共同方法：检验与推断

Propensity score with factor loadings: evaluating the effect of the Paris Agreement on the returns of European stocks

Angelo Forino · Andrea Mercatanti · Giacomo Morelli首次提交 2025-07-11 · 修订 2026-10-14 · v2arXiv预印本

Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel method for panel causal inference within a super-population framework, based on…

微观 · 因果推断微观 · 面板数据金融 · 金融因子

被引：暂无数据

阅读原文 ↗BibTeX ↓

共同方法：检验与推断

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural vector autoregressions

Paritosh Shankarrao Junare首次提交 2026-07-19 · 修订 2026-10-14 · v2arXiv预印本

Normality pre-tests on reduced-form innovations cannot detect whether two or more underlying structural shocks are Gaussian, and hence cannot detect a failure of identification in non-Gaussian SVARs. We propose a bootstrap-based approach that reveals such a failure. Under the null of valid identification and certain regularity conditions, the conditional bootstrap distribution of the maximum likelihood estimator of the impact matrix is asymptotically normal, so the…

宏观 · 时间序列 Bootstrap 与重抽样极大与准极大似然

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-15

阅读原文 ↗BibTeX ↓

共同方法：检验与推断

Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$-values

Jinyuan Chang · Chenlong Li · Cheng Yong Tang · Zhengtian Zhu首次提交 2025-05-22 · 修订 2026-10-14 · v3arXiv预印本

结合 model-X knockoffs 与 p 值的高维线性模型 FDR 控制AI 解读

We propose a novel multiple testing methodology for controlling the false discovery rate (FDR) in high-dimensional linear models that integrates model-X knockoff techniques with debiased penalized regression estimators. At the foundation of our methodology, we construct and study two sets of naturally paired high-dimensional test statistics and the associated $p$-values for evaluating the same null hypotheses. The first set is shown to be asymptotically mutually independent,…

多重检验与同时推断正则化与变量选择

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-14

AI 解读阅读原文 ↗BibTeX ↓