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局部稳健且半参数有效的贝叶斯推断AI 解读

We propose a framework for making Bayesian parametric models robust to local misspecification. Suppose in a baseline parametric model, a parameter of interest has an interpretation in an encompassing semiparametric model. Bayesian and maximum likelihood estimators are generally biased under local misspecification. We propose to augment the baseline likelihood by a multiplicative factor that involves scores for the baseline model, the efficient scores for the encompassing…

动态网络的双重异质性模型AI 解读

Analysis of networks that evolve dynamically requires the joint modelling of individual snapshots and time dynamics. This paper proposes a new flexible two-way heterogeneity model towards this goal. The new model equips each node of the network with two heterogeneity parameters, one to characterize the propensity to form ties with other nodes statically and the other to differentiate the tendency to retain existing ties over time. With n observed networks each having p…

Abstract For data segmentation in high-dimensional linear regression settings, the regression parameters are often assumed to be exactly sparse segment-wise, which enables many existing methods to estimate the parameters locally via ℓ1-regularized maximum-likelihood-type estimation and then contrast them for change point detection. Contrary to this common practice, we show that the exact sparsity of neither regression parameters nor their differences, a.k.a. differential…

列子集选择的统计视角AI 解读

Abstract We consider the problem of selecting a small subset of representative variables from a large dataset. In the computer science literature, this dimensionality reduction problem is typically formalized as column subset selection (CSS). Meanwhile, the typical statistical formalization is to find an information-maximizing set of principal variables. This paper shows that these two approaches are equivalent, and moreover, both can be viewed as maximum-likelihood…

利用 Copula 处理空间自回归模型中的内生性AI 解读

This paper develops a novel, instrument-free semiparametric copula framework for a spatial autoregressive (SAR) model to address endogeneity arising from an endogenous spatial weights matrix, endogenous regressors, or both. Moving beyond conventional Gaussian copulas, we develop a flexible estimator based on the Student’s t copula with an unknown degrees-of-freedom (df) parameter, which nests the Gaussian case and allows the data to determine the presence of tail dependence.…

使用大量 L 矩的模型参数推断AI 解读

This paper studies parameter estimation using L-moments, an alternative to traditional moments with attractive statistical properties. The estimation of model parameters by matching sample L-moments is known to outperform maximum likelihood estimation (MLE) in small samples from popular distributions. The choice of the number of L-moments used in estimation remains ad-hoc, though: researchers typically set the number of L-moments equal to the number of parameters, which is…

过滤非平稳成分后的半参数动态 Copula 模型改进估计AI 解读

This paper considers estimation of short-run dynamics in time series that contain a nonstationary component. We assume that appropriate preliminary methods can be applied to the observed time series to separate short-run elements from long-run slowly evolving secular components, and focus on estimation of the short-run dynamics based on the filtered data. We use a flexible copula-generated Markov model to capture the nonlinear temporal dependence in the short-run component…

双边关系模型中遗漏异质性的检验AI 解读

We develop a Lagrange Multiplier (LM) test of neglected heterogeneity in dyadic models. The test statistic is derived by modifying Breusch and Pagan (1980)’s test. We establish the asymptotic distribution of the test statistic under the null using a novel martingale construction. We also consider the power of the LM test in generic panel models. Even though the test is motivated by random effects, we show that it has a power for detecting fixed effects as well. Finally, we…

状态切换整数值自回归模型AI 解读

Time series of counts often display complex dynamic and distributional characteristics. For this reason, we develop a flexible framework combining the integer-valued autoregressive (INAR) model with a latent Markov structure, leading to the hidden Markov model-INAR (HMM-INAR). First, we illustrate conditions for the existence of an ergodic and stationary solution and derive closed-form expressions for the autocorrelation function and its components. Second, we show…

维度递增随机图模型中的似然比检验AI 解读

We explore the Wilks phenomena in two random graph models: the β-model and the Bradley–Terry model. For two increasing dimensional null hypotheses, including a specified null H0:βi=βi0 for i=1,…,r and a homogenous null H0:β1=⋯=βr, we reveal high dimensional Wilks’ phenomena that the normalized log-likelihood ratio statistic, [2{l(β̂)−l(β̂0)}−r]/(2r)1/2, converges in distribution to the standard normal distribution as r goes to infinity. Here, l(β) is the log-likelihood…

含吸收态的区间删失多状态数据半参数回归分析AI 解读

In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertained through periodic examinations and thus interval-censored. The time of reaching the absorbing state is known or right-censored, with the transient state at the previous instant being unobserved. In this paper, we provide a general…

含随机变点的区间删失失效时间数据估计与变量选择AI 解读

Motivated by a breast cancer study, we consider regression analysis of interval-censored failure time data in the presence of a random change point. Although a great deal of literature on interval-censored data has been established, there does not seem to exist an established method that can allow for the existence of random change points. Such data can occur in, for example, clinical trials where the risk of a disease may dramatically change when some biological indexes of…

广义自回归条件贝塔：多因子资产定价中的纵向反馈AI 解读

We propose a new class of observation-driven models, Generalized Autoregressive Conditional Betas, which describe the joint dynamics of time-varying slopes in a system of conditionally heteroskedastic simultaneous multiple regressions. The model accommodates large dimensions, parametric longitudinal restrictions, exogenous variables, and the coexistence of constant and time-varying slopes. It also introduces new mechanisms for the transmission of shocks, namely, beta…

时空点过程的部分线性单指标模型与函数主成分分析AI 解读

We model spatially and temporally indexed point process data as a multi-level log-Gaussian Cox process where the log intensity function depends on a partially linear single-index structure of spatio-temporal covariates and three latent functional random effects representing the spatial and temporal random effects as well as their interactions. We assume that the latent functional effects are Gaussian processes with Karhunen-Loève representations, and model the unknown link…

金融时间序列截面最大值的自回归条件 GB2 动态建模AI 解读

This article introduces the “autoregressive conditional generalized beta distribution of the second kind” (AcGB2) to model the dynamic cross-sectional maxima of multivariate financial times series. The temporal dependence of the resulting univariate maxima time series is characterized by the parameter dynamics of the standard GB2 distribution, which offer versatility in approximating various distributions, including heavy-tailed distributions, through parameter adjustments.…

Plackett–Luce 模型中似然估计量的统一分析AI 解读

The Plackett–Luce model has been extensively used for rank aggregation in social choice theory. A central statistical question in this model concerns estimating the utility vector that governs the model’s likelihood. In this paper, we investigate the asymptotic theory of utility vector estimation by maximizing different types of likelihood, such as full, marginal, and quasi-likelihood. Starting from interpreting the estimating equations of these estimators to gain some…

高维广义线性模型的贝叶斯模型选择一致性进展AI 解读

Uncovering genuine relationships between a response variable and a large collection of covariates is a fundamental and practically important problem. In the context of Gaussian linear models, both the Bayesian and non-Bayesian literature is well-developed and there are no substantial differences in the model selection consistency results available from the two schools. For generalized linear models (GLMs), however, Bayesian model selection consistency results are lacking in…

函数型图形套索AI 解读

We consider the problem of recovering conditional independence relationships between p jointly distributed Hilbertian random elements given n realizations thereof. We operate in the sparse high-dimensional regime, where n ≪ p and no element is related to more than d ≪ p other elements. In this context, we propose an infinite-dimensional generalization of the graphical lasso. We prove model selection consistency under natural assumptions and extend many classical results to…

协变量依赖转移概率的隐马尔可夫状态下，混合模型的稳健性AI 解读

This article studies the robustness of quasi-maximum-likelihood estimation in hidden Markov models when the regime-switching structure is misspecified. Specifically, we examine the case where the data-generating process features a hidden Markov regime sequence with covariate-dependent transition probabilities, but estimation proceeds under a simplified mixture model that assumes regimes are independent and identically distributed. We show that the parameters governing the…

需求的实解析离散选择模型：理论与含义AI 解读

We demonstrate that a large class of discrete choice models of demand can be approximated by real analytic demand models. We obtain this result by combining (i) a novel real analytic property of the mixed logit and the mixed probit models with any distribution of random coefficients and (ii) an approximation property of finite mixtures of Gumbel and Gaussian distributions. To illustrate some of the implications of this result, we discuss how real analyticity facilitates…

不依赖序列排序的得分驱动动态因子模型AI 解读

This paper introduces a novel score-driven dynamic factor model designed for filtering cross-sectional co-movements in panels of time series. The model is formulated using elliptical distribution for noise terms, allowing the update of the time-varying parameter to be potentially nonlinear and robust to outliers. We derive stochastic properties of time series generated by the model, such as stationarity and ergodicity, and establish the invertibility of the filter. We prove…

含结构变化的空间面板数据模型AI 解读

Spatial panel data models have gained widespread application in social sciences, particularly in economics, due to their ability to capture spatial dependencies. However, existing studies on spatial models typically rely on the assumption of parameter stability, which may be overly restrictive given the well-documented prevalence of structural changes in economic relationships. This paper addresses this limitation by proposing and analyzing spatial panel data models that…

一般一阶方法的逐坐标动态与普适性AI 解读

General first-order methods (GFOMs), including many variants of gradient descent and approximate message passing algorithms, constitute a broad class of iterative algorithms widely applied in modern statistical learning problems. Some GFOMs also serve as constructive proof devices, iteratively characterizing the empirical distributions of statistical estimators in the asymptotic regime of large system limits for any fixed number of iterations. This paper develops a…

固定与随机协变量下的协方差回归分析AI 解读

Covariance regression analysis is an approach to linking the covariance of responses to a set of explanatory variables X, where X can be a vector, matrix, or tensor. Most of the literature on this topic focuses on the “Fixed-X” setting and treats X as nonrandom. By contrast, treating explanatory variables X as random, namely the “Random-X” setting, is often more realistic in practice. This article aims to fill this gap in the literature on the estimation and model assessment…