论文检索

摘要暂缺

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…

摘要暂缺

摘要暂缺

摘要暂缺

摘要暂缺

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(·)…

摘要暂缺

摘要暂缺

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.

摘要暂缺

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.

摘要暂缺

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.

摘要暂缺

lq球对称模型中的稳健贝叶斯推断AI 解读

The class of multivariate lq-spherical distributions is introduced and defined through their isodensity surfaces. We prove that, under a Jeffrey's type improper prior on the scale parameter, posterior inference on the location parameters is the same for all lq-spherical sampling models with common q. This gives us perfect inference robustness with respect to any departures from the reference case of independent sampling from the exponential power distribution.

摘要暂缺