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基于对数正态形状混合分布的客观贝叶斯生存分析AI 解读

Survival models such as the Weibull or log-normal lead to inference that is not robust to the presence of outliers. They also assume that all heterogeneity between individuals can be modeled through covariates. This article considers the use of infinite mixtures of lifetime distributions as a solution for these two issues. This can be interpreted as the introduction of a random effect in the survival distribution. We introduce the family of shape mixtures of log-normal…

面板数据的非高斯动态贝叶斯建模AI 解读

Abstract A first order autoregressive non‐Gaussian model for analysing panel data is proposed. The main feature is that the model is able to accommodate fat tails and also skewness, thus allowing for outliers and asymmetries. The modelling approach is designed to gain sufficient flexibility, without sacrificing interpretability and computational ease. The model incorporates individual effects and covariates and we pay specific attention to the elicitation of the prior. As…

基于偏斜t分布的非高斯面板数据模型聚类AI 解读

We propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behavior, equilibrium level, and the effect of covariates. Inference is addressed from a Bayesian perspective, and model comparison is conducted using Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors…

通过超球面对数样条建模方向性离散程度AI 解读

Summary We introduce the directionally dispersed class of multivariate distributions, a generalization of the elliptical class. By allowing dispersion of multivariate random variables to vary with direction it is possible to generate a very wide and flexible class of distributions. Directionally dispersed distributions have a simple form for their density, which extends a spherically symmetric density function by including a function D modelling directional dispersion. Under…

跨国增长回归中的模型不确定性AI 解读

Abstract We investigate the issue of model uncertainty in cross‐country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is spread widely among many models, suggesting the superiority of BMA over choosing any single model. Out‐of‐sample predictive results support this claim. In contrast to Levine and Renelt ( 1992 ), our results broadly support the more ‘optimistic’ conclusion of Sala‐i‐Martin ( 1997b ), namely that some…

典型求职搜寻模型的贝叶斯分析AI 解读

Bayesian analysis for a simple but widely applied dynamic programming model is obtained. The setting is the prototypal job-search model. The general case of wage and duration data, with potential censoring, is studied. The optimality condition implied by the dynamic programming setup is fully imposed. The posterior distribution reveals a 'ridge' reflecting the characteristic nonstandard nature of the inference problem. Marginal distributions and moments are obtained in a…

厚尾与偏态的贝叶斯建模AI 解读

We consider a Bayesian analysis of linear regression models that can account for skewed error distributions with fat tails. The latter two features are often observed characteristics of empirical datasets, and we formally incorporate them in the inferential process. A general procedure for introducing skewness into symmetric distributions is first proposed. Even though this allows for a great deal of flexibility in distributional shape, tail behavior is not affected.…

厚尾与偏度的贝叶斯建模AI 解读

We consider a Bayesian analysis of linear regression models that can account for skewed error distributions with fat tails.The latter two features are often observed characteristics of empirical data sets, and we will formally incorporate them in the inferential process.A general procedure for introducing skewness into symmetric distributions is rst proposed.Even though this allows for a great deal of exibility in distributional shape, tail behaviour is not aected.In…

多元回归模型中经典与贝叶斯推断的稳健性AI 解读

We consider a one-to-one correspondence between points z ∈ Rn - {0} and pairs (y, r), where r > 0 and y lies in some space y, through z = r y. As an immediate consequence, we can represent random variables Z that take values in Rn - {0} as Z = R Y, where R is a positive random variable and Y takes values in y. By fixing the distribution of either R or Y while imposing independence between them, we generate classes of distributions on Rn . Many families of multivariate…

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υ-球形分布的建模与推断AI 解读

A new class of continuous multivariate distributions on × ∈ ℜ n is proposed. We define these so-called υ-spherical distributions through properties of the density function in a location-scale context. We derive conditions for properness of υ-spherical distributions and discuss how to generate them in practice. The name “υ-spherical” is motivated by the fact that these distributions generalize the classes of spherical (when υ(·) is the l 2 norm) and l q -spherical (when υ(·)…

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重访连续多元位置—尺度模型：稳健性研究AI 解读

SUMMARY We consider the location-scale family of all multivariate continuous sampling distributions. Any model from the subclass induced by fixing a particular distribution on the unit sphere, combined with a standard improper prior on the scale parameter, will lead to the same inference on location and observables. This finding sheds light on many robustness results in less general models and its implications are illustrated in a number of examples.

灵活函数形式下的贝叶斯效率分析：AIM 成本函数AI 解读

In this article, the authors describe the use of Gibbs sampling methods for drawing posterior inferences in a cost frontier model with an asymptotically ideal price aggregator, nonconstant returns to scale, and composed error. An empirical example illustrates the sensitivity of efficiency measures to assumptions made about the functional form of the frontier. The authors also examine the consequences of imposing regularity through parametric restrictions alone.

基于椭圆模型混合的单位根假说决策理论分析AI 解读

This paper develops a formal decision theoretic approach to testing for a unit root in economic time series. The approach is empirically implemented by specifying a loss function based on predictive variances; models are chosen so as to minimize expected loss. In addition, the paper broadens the class of likelihood functions traditionally considered in the Bayesian unit root literature. Empirical results indicate that, while the posterior probability of trend-stationarity is…

重新审视连续多元位置—尺度模型的稳健性AI 解读

We consider the location-scale family of all multivariate continuous sampling distributions. Any model from the subclass induced by fixing a particular distribution on the unit sphere, combined with a standard improper prior on the scale parameter, will lead to the same inference on location and observables. This finding sheds light on many robustness results in less general models and its implications are illustrated in a number of examples.

球对称模型中的稳健贝叶斯推断AI 解读

The class of multivariate I4-spherical di,tributions is introduced and defined through their isodensity surfaces.We prove that, under a JeHreys' type improper prior on the scale parameter, posterior inference on the location parameters is the same for all Iq-spherical sampling models with common q.7itis gives us perfect inference robustness with respect to any departures from the reference case of independcnt sampling from the exponential power distribution.

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