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非光滑备择下的最优极小极大速率AI 解读

Summary This study investigates optimal minimax rates for specification testing when the alternative hypothesis is built on a set of nonsmooth functions. The set consists of bounded functions that are not necessarily differentiable with no smoothness constraints imposed on their derivatives. In the instrumental variable regression set up with an unknown error variance structure, we find that the optimal minimax rate is $n^{-1/4}$, where n is the sample size. The rate is…

专利、数据独占与新药开发AI 解读

Abstract Pharmaceutical firms enjoy market exclusivity for new drugs from concurrent patent protection and exclusivity of the clinical trials data submitted for market approval. Patent invalidation during drug development renders data exclusivity the sole source of protection and shifts the period of market exclusivity. In instrumental variables regressions, we quantify the effect of a one-year reduction in expected market exclusivity on the likelihood of drug…

通过异方差识别的模型中的稳健推断AI 解读

Abstract Identification via heteroskedasticity exploits variance changes between regimes to identify parameters in simultaneous equations. Weak identification occurs when shock variances change very little or multiple variances change close to proportionally, making standard inference unreliable. I propose an F-test for weak identification in a common simple version of the model. More generally, I establish conditions for validity of nonconservative robust inference on…

含大量内生变量的高维线性模型AI 解读

High-dimensional linear models with endogenous variables play an increasingly important role in the recent econometric literature. In this work, we allow for models with many endogenous variables and make use of many instrumental variables to achieve identification. Because of the high-dimensionality in the structural equation, constructing honest confidence regions with asymptotically correct coverage is non-trivial. Our main contribution is to propose estimators and…

代理 SVAR 的稳健贝叶斯推断AI 解读

We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the parameters of interest are set-identified using external instruments, or ‘proxy SVARs’. Set-identification in these models typically occurs when there are multiple instruments for multiple structural shocks. Existing Bayesian approaches to inference in proxy SVARs require researchers to specify a single prior over the model’s parameters, but, under set-identification, a…

结构神经影像标签融合的贝叶斯空间二元回归AI 解读

Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or…

通过序贯嵌套回归模型分析妊娠结局AI 解读

The polycystic ovary syndrome (PCOS) is a most common cause of infertility among women of reproductive age. Unfortunately, the etiology of PCOS is poorly understood. Large-scale clinical trials for pregnancy in polycystic ovary syndrome (PPCOS) were conducted to evaluate the effectiveness of treatments. Ovulation, pregnancy, and live birth are three sequentially nested binary outcomes, typically analyzed separately. However, the separate models may lose power in detecting…

无向图模型中的贝叶斯边回归：刻画癌症患者间异质性AI 解读

It is well-established that interpatient heterogeneity in cancer may significantly affect genomic data analyses and in particular, network topologies. Most existing graphical model methods estimate a single population-level graph for genomic or proteomic network. In many investigations, these networks depend on patient-specific indicators that characterize the heterogeneity of individual networks across subjects with respect to subject-level covariates. Examples include…

连续非线性交互作用稳健检验的交叉验证集成方法AI 解读

Gene-environment and nutrition-environment studies often involve testing of high-dimensional interactions between two sets of variables, each having potentially complex nonlinear main effects on an outcome. Construction of a valid and powerful hypothesis test for such an interaction is challenging, due to the difficulty in constructing an efficient and unbiased estimator for the complex, nonlinear main effects. In this work, we address this problem by proposing a…

观察性研究中结局依赖抽样的因果效应界AI 解读

Outcome-dependent sampling designs are common in many different scientific fields including epidemiology, ecology, and economics. As with all observational studies, such designs often suffer from unmeasured confounding, which generally precludes the nonparametric identification of causal effects. Nonparametric bounds can provide a way to narrow the range of possible values for a nonidentifiable causal effect without making additional untestable assumptions. The nonparametric…

Hellinger 相关系数AI 解读

In this article, the defining properties of any valid measure of the dependence between two continuous random variables are revisited and complemented with two original ones, shown to imply other usual postulates. While other popular choices are proved to violate some of these requirements, a class of dependence measures satisfying all of them is identified. One particular measure, that we call the Hellinger correlation, appears as a natural choice within that class due to…

结构方程模型平均：方法与应用AI 解读

The instrumental variable (IV) methods are attractive since they can lead to a consistent answer to the main question in causal modeling, that is, the estimation of average causal effect of an exposure on the outcome in the presence of unmeasured confounding. However, it is now acknowledged in the literature that using weak IVs might not suit the inference goal satisfactorily. We consider the problem of estimating causal effects in an observational study in this article,…

SIMPLE：大型网络中社群成员构成的统计推断AI 解读

Abstract Network data are prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remains largely unexplored. In this paper, we propose the method of statistical inference on membership profiles in large networks (SIMPLE) in the setting…

通过多元谱的总Frobenius范数进行模型识别AI 解读

Abstract We study the integral of the Frobenius norm as a measure of the discrepancy between two multivariate spectra. Such a measure can be used to fit time series models, and ensures proximity between model and process at all frequencies of the spectral density—this is more demanding than Kullback–Leibler discrepancy, which is instead related to one-step ahead forecasting performance. We develop new asymptotic results for linear and quadratic functionals of the…

含隐变量的多响应回归自适应估计AI 解读

A prominent concern of scientific investigators is the presence of unobserved hidden variables in association analysis. Ignoring hidden variables often yields biased statistical results and misleading scientific conclusions. Motivated by this practical issue, this paper studies the multivariate response regression with hidden variables, Y=(Ψ∗)TX+(B∗)TZ+E, where Y∈Rm is the response vector, X∈Rp is the observable feature, Z∈RK represents the vector of unobserved hidden…

非稀疏高维线性模型的可检验性AI 解读

Understanding statistical inference under possibly nonsparse high-dimensional models has gained much interest recently. For a given component of the regression coefficient, we show that the difficulty of the problem depends on the sparsity of the corresponding row of the precision matrix of the covariates, not the sparsity of the regression coefficients. We develop new concepts of uniform and essentially uniform nontestability that allow the study of limitations of tests…

得分驱动模型的最大似然估计AI 解读

We establish strong consistency and asymptotic normality of the maximum likelihood estimator for stochastic time-varying parameter models driven by the score of the predictive conditional likelihood function. For this purpose, we formulate primitive conditions for global identification, invertibility, strong consistency, and asymptotic normality both under correct specification and misspecification of the model. A detailed illustration is provided for a conditional…

漂移突增假说AI 解读

The drift burst hypothesis postulates the existence of short-lived locally explosive trends in the price paths of financial assets. The recent U.S. equity and treasury flash crashes can be viewed as two high-profile manifestations of such dynamics, but we argue that drift bursts of varying magnitude are an expected and regular occurrence in financial markets that can arise through established mechanisms of liquidity provision. We show how to build drift bursts into the…

残差增广的 IVX 预测回归AI 解读

Bias correction in predictive regressions is known to reduce the empirical size problems of OLS-based predictability tests with persistent predictors. This paper shows that bias correction is also achieved in the context of the extended instrumental variable (IVX) predictability testing framework introduced by Kostakis et al. (2015). To be specific, new IVX-based statistics subject to a bias correction analogous to that proposed by Amihud and Hurvich (2004) are introduced.…

半参数识别与费舍尔信息AI 解读

This paper provides a systematic approach to semiparametric identification that is based on statistical information as a measure of its “quality.” Identification can be regular or irregular, depending on whether the Fisher information for the parameter is positive or zero, respectively. I first characterize these cases in models with densities linear in an infinite-dimensional parameter. I then introduce a novel “generalized Fisher information.” If positive, it implies…

Brenier 映射势函数的收敛速度AI 解读

The theory of optimal transportation has experienced a sharp increase in interest in many areas of economic research such as optimal matching theory and econometric identification. A particularly valuable tool, due to its convenient representation as the gradient of a convex function, has been the Brenier map: the matching obtained as the optimizer of the Monge–Kantorovich optimal transportation problem with the euclidean distance as the cost function. Despite its…

结构多变量 GARCH 模型的识别AI 解读

The class of multivariate GARCH models is widely used to quantify and monitor volatility and correlation dynamics of financial time series. While many specifications have been proposed in the literature, these models are typically silent about the system inherent transmission of implied orthogonalized shocks to vector returns. In a framework of non-Gaussian independent structural shocks, this paper proposes a loss statistic, based on higher order co-moments, to discriminate…

股票收益阶段性可预测性的检验AI 解读

Standard tests based on predictive regressions estimated over the full available sample data have tended to find little evidence of predictability in stock returns. Recent approaches based on the analysis of subsamples of the data suggest in fact that predictability where it occurs might exist only within so-called “pockets of predictability” rather than across the entire sample. However, these methods are prone to the criticism that the subsample dates are endogenously…

误分类下流行率与趋势估计的贝叶斯方法AI 解读

Abstract In this paper, we present a Bayesian approach to estimate the mean of a binary variable and changes in the mean over time, when the variable is subject to misclassification error. These parameters are partially identified, and we derive identified sets under various assumptions about the misclassification rates. We apply our method to estimating the prevalence and trend of prescription opioid misuse, using data from the 2002–2014 National Survey on Drug Use and…