Probability is a useful tool for describing uncertainty, so it is natural to strive for a system of statistical inference based on probabilities for or against various hypotheses. But existing probabilistic inference methods struggle to provide a meaningful interpretation of the probabilities across experiments in sufficient generality. In this paper we further develop a promising new approach ...

Abstract Generative adversarial networks (GANs) have been impactful on many problems and applications but suffer from unstable training. The Wasserstein GAN (WGAN) leverages the distance to avoid caveats in minmax two-player training of GANs has other defects such as mode collapse lack metric detect convergence. We introduce a novel inferential (iWGAN) model, which is principled framework fuse ...

In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this new estimator and mixed ridge estimator (MRE) against th...

The current article represents the further validation of the construct of inferential confusion amongst clinical samples. Inferential confusion is proposed to be a meta-cognitive confusion particularly relevant to obsessive compulsive disorder (OCD) that leads a person to confuse an imagined possibility with an actual probability. As such, it conceptualizes OCD as a form of belief disorder simi...

Substantial structure and restrictions, such as monotonicity and curvature constraints, necessary to give economic interpretation to empirical findings are often furnished by economic theories. Although such restrictions may be imposed in certain parametric empirical settings in a relatively straightforward fashion, incorporating such restrictions in semiparametric models is often problematic. ...

A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomize ODE solvers by adding a random forcing term, we show that a probability measure over the numerical solution of ODEs can be obtained by introducing suitable random time-steps in a classic...

This paper introduces the notion of robust extremes in deterministic chaotic systems, presents initial theoretical results, and outlines associated inferential techniques. A chaotic deterministic system is said to exhibit robust extremes under a given observable when the associated statistics of extreme values depend smoothly on the system's control parameters. Robust extremes are here illustra...

Some work has been done in the past on the estimation of parameters of the threeparameter lognormal distribution based on complete and censored samples. In this article, we develop inferential methods based on progressively Type-II censored samples from a three-parameter lognormal distribution. In particular, we use the EM algorithm as well as some other numerical methods to determine maximum l...

We propose an estimating function method for two related applications, matchedpair studies and studies with errors-in-covariates under a functional model, where a mismeasured unknown scalar covariate is treated as a xed nuisance parameter. Our method addresses the severe inferential problem posed by an abundance of nuisance parameters in these two applications. We propose orthogonal locally anc...

Skew normal mixture models provide a more flexible framework than the popular normal mixtures for modelling heterogeneous data with asymmetric behaviors. Due to the unboundedness of likelihood function and the divergency of shape parameters, the maximum likelihood estimators of the parameters of interest are often not well defined, leading to dissatisfactory inferential process. We put forward ...