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部分观测多元数据的边缘检测、空间平滑与图像重建AI 解读

Situations with incomplete multivariate spatial data on a lattice are considered. The goal is to impute the missing data in the presence of edges or boundaries and recover the image. Two methods based on Bayesian hierarchical models that iterate between edge detection and spatial smoothing to impute the missing data within identified homogeneous regions are examined. Their performance is compared with another method that imputes the missing values using edge-preserving…

关联私人价值下第一价格拍卖的非对称性AI 解读

Abstract Collusion and heterogeneity across firms may introduce asymmetry in bidding games. A major difficulty in asymmetric auctions is that the Bayesian Nash equilibrium strategies are solutions of an intractable system of differential equations. We propose a simple method for estimating asymmetric first‐price auctions with affiliated private values. Considering two types of bidders, we show that these differential equations can be rewritten using the observed bid…

对 Brooks、Giudici 与 Roberts 论文的讨论AI 解读

This paper aims to develop general strategies for improving jumps between models in reversible jump Markov chain Monte Carlo (MCMC) algorithms, which is quite an important and timely goal. Indeed, in practical implementations of the method, we usually find that the choice of proposals is paramount: in many cases, the ‘natural choice’ leads to a zero acceptance probability and the construction of well-tuned moves is often quite costly. Given that the reversible jump MCMC…

单调缺失纵向数据分析中的模式混合模型与选择模型AI 解读

Summary We examine three pattern–mixture models for making inference about parameters of the distribution of an outcome of interest Y that is to be measured at the end of a longitudinal study when this outcome is missing in some subjects. We show that these pattern–mixture models also have an interpretation as selection models. Because these models make unverifiable assumptions, we recommend that inference about the distribution of Y be repeated under a range of plausible…

利用因果图选择协变量以估计控制方案的因果效应AI 解读

Summary Consider a case where cause–effect relationships between variables can be described by a causal path diagram and the corresponding linear structural equation model. The paper proposes a graphical selection criterion for covariates to estimate the causal effect of a control plan. For designing the control plan, it is essential to determine both covariates that are used for control and covariates that are used for identification. The selection of covariates used for…

多元混合模型中成分分布的非参数估计AI 解读

Suppose k-variate data are drawn from a mixture of two distributions, each having independent components. It is desired to estimate the univariate marginal distributions in each of the products, as well as the mixing proportion. This is the setting of two-class, fully parametrized latent models that has been proposed for estimating the distributions of medical test results when disease status is unavailable. The problem is one of inference in a mixture of distributions…

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归纳推断：公理化方法AI 解读

A predictor is asked to rank eventualities according to their plausibility, based on past cases. We assume that she can form a ranking given any memory that consists of repetitions of past cases. Mild consistency requirements on these rankings imply that they have a numerical representation via a matrix assigning numbers to eventualitycase pairs, as follows. A memory is identified with a vector, counting the number of repetitions of each case. Multiplication of the matrix by…

间接推断、干扰参数与门限移动平均模型AI 解读

nuisance parameter is not identified under the null hypothesis. We develop a testing procedure adapted to this simulation-based estimation method, and detail its use for detecting the threshold effect in threshold moving average models with contemporaneous and lagged asymmetries. In contrast to existing threshold models, these models allow taking into account the presence of asymmetric effects of current and lagged random shocks. We use them to measure the persistence of…

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存在异质性时非参数捕获—再捕获模型的新贝叶斯方法AI 解读

The intrinsic heterogeneity of individuals is a potential source of bias in estimation procedures for capture‐recapture models. To account for this heterogeneity in the model a hierarchical structure has been proposed whereby the probabilities that each animal is caught on a single occasion are modelled as independent draws from a common unknown distribution F. However, there is general agreement that modelling F by a simple parametric curve may lead to unsatisfactory…

时变参数随机波动率均值模型的矩与动态结构AI 解读

This paper introduces and discusses some of the statistical properties of a time-varying parameter stochastic volatility (SV) in mean model. We derive the autocovariance function of an observed series, under the assumption that the conditional variance follows a flexible parameterization, which nests the autoregressive SV and the exponential GARCH specifications. Furthermore, the mean parameter can be time varying. We also present the autocovariance functions of higher…

发现与癌症相关的基因组异常组合AI 解读

This article introduces a model-based statistical methodology for the analysis of copy-number variations in cancer genomes measured by comparative genomic hybridization. The methodology allows one to infer combinations of genomic aberrations associated with the cancer phenotype. The stochastic model conjoins two features of cancer biology to infuse some context into an otherwise unsupervised learning problem. It asserts random genomic instability in a potential progenitor…

季节性非线性自回归模型的非参数与半参数识别AI 解读

Non- or semiparametric estimation and lag selection methods are proposed for three seasonal nonlinear autoregressive models of varying seasonal flexibility. All procedures are based on either local constant or local linear estimation. For the semiparametric models, after preliminary estimation of the seasonal parameters, the function estimation and lag selection are the same as nonparametric estimation and lag selection for standard models. A Monte Carlo study demonstrates…

标准拍卖模型的识别AI 解读

We present new identification resiilts for models of first-price, second-price, ascending (English), and descending (Dutch) auctions.We analyze a general specification of bidders' preferences and the underlying information structure, nesting as special cases the pure private values and pure common values models, and allowing both ex ante symmetric and asymmetric bidders.We address identification of a series of such models and propose strategies for discriminating between…

博弈中人类学习的两种竞争模型AI 解读

Reinforcement learning and stochastic fictitious play are apparent rivals as models of humans learning. They embody quite different assumptions about the processing of information and optimisation. This paper compares their properties and finds that they are far more similar than were thought. In particular, the expected motion of stochastic fictitious play and reinforcement learning with experimentation can both be written as a perturbed form of the evolutionary replicator…

Reinforcement learning and stochastic fictitious play are apparent rivals as models of human learning. They embody quite different assumptions about the processing of information and optimization. This paper compares their properties and finds that they are far more similar than were thought. In particular, the expected motion of stochastic fictitious play and reinforcement learning with experimentation can both be written as a perturbed form of the evolutionary replicator…

This paper presents new identification results for models of first–price, second–price, ascending (English), and descending (Dutch) auctions. We consider a general specification of the latent demand and information structure, nesting both private values and common values models, and allowing correlated types as well as ex ante asymmetry. We address identification of a series of nested models and derive testable restrictions enabling discrimination between models on the basis…

实验杂交中数量性状基因座识别的模型选择方法AI 解读

Summary We consider the problem of identifying the genetic loci (called quantitative trait loci (QTLs)) contributing to variation in a quantitative trait, with data on an experimental cross. A large number of different statistical approaches to this problem have been described; most make use of multiple tests of hypotheses, and many consider models allowing only a single QTL. We feel that the problem is best viewed as one of model selection. We discuss the use of model…

单观测无偏先验AI 解读

This paper studies a class of default priors, which we call single observation unbiased priors (SOUP). A prior for a parameter is a SOUP if the corresponding posterior mean of the parameter based on a single observation is an unbiased estimator of the parameter. We prove that, under mild regularity conditions, a default prior for a convolution parameter is "noninformative" in the sense of yielding a posterior inference invariant under amalgamation only if it is a SOUP.…

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参数位于边界时的广义矩估计AI 解读

This article establishes the asymptotic distributions of generalized method of moments (GMM) estimators when the true parameter lies on the boundary of the parameter space. The conditions allow the estimator objective function to be nonsmooth and to depend on preliminary estimators. The boundary of the parameter space may be curved and/or kinked. The article discusses three examples: (1) instrumental variables (IV) estimation of a regression model with nonlinear equality…

Sargan 的工具变量估计与广义矩方法AI 解读

This article surveys J. D. Sargan's work on instrumental variables (IV) estimation and its connections with the generalized method of moments (GMM). First the modeling context in which Sargan motivated IV estimation is presented. Then the theory of IV estimation as developed by Sargan is discussed. His approach to efficiency, his minimax estimator, tests of overidentification and underidentification, and his later work on the finite-sample properties of IV estimators are…

广义矩方法中的弱工具变量与弱识别综述AI 解读

Weak instruments arise when the instruments in linear instrumental variables (IV) regression are weakly correlated with the included endogenous variables. In generalized method of moments (GMM), more generally, weak instruments correspond to weak identification of some or all of the unknown parameters. Weak identification leads to GMM statistics with nonnormal distributions, even in large samples, so that conventional IV or GMM inferences are misleading. Fortunately, various…