A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make parametric assumptions about the form of the encoder distribution. Performance is further enhanced by integrating the proposed encoder with importance sampling. Excellent performance is demonstrated across multiple unsupe...

A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make parametric assumptions about the form of the encoder distribution. Performance is further enhanced by integrating the proposed encoder with importance sampling. Excellent performance is demonstrated across multiple unsupe...

Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term's distribution is assumed to be Asymmetr...

Parametric approaches to Learning, such as deep learning (DL), are highly popular in nonlinear regression, in spite of their extremely difficult training with their increasing complexity (e.g. number of layers in DL). In this paper, we present an alternative semi-parametric framework which foregoes the ordinarily required feedback, by introducing the novel idea of geometric regularization. We s...

We present a Bayesian decision theoretic approach for developing replacament strategies. In so doing, we consider a semi-parametric model to describe the failure characteristics of systems by specifying a nonparametric form for cumulative intensity function and by taking into account effect of covariates by a parametric form. Use of a gamma process prior for the cumulative intensity function co...

The piecewise-constant exponential model is a semi-parametric continuous time duration model.1 The model is semi-parametric in the sense that the shape of the hazard rate with respect to time is not specified a priori but is determined by the data. The basic idea underlying the PCE model is that the duration time can be divided up into discrete units in each of which the hazard rate is assumed ...

Let Q be the field of rational numbers and Q[u1, ..., ud, x1, ..., xs] the ring of polynomials in n indeterminates with coefficients in Q and d + s = n (0 ≤ d < n). The indeterminates are divided into two groups: u = (u1, ..., ud) and x = (x1, ..., xs), which are called parameters and variables, respectively. A polynomial set is a finite set of nonzero polynomials in Q[u,x]. The following syste...

BACKGROUND Competing risks arise when the subject is exposed to more than one cause of failure. Data consists of the time that the subject failed and an indicator of which risk caused the subject to fail. METHODS With three approaches consisting of Fine and Gray, binomial, and pseudo-value, all of which are directly based on cumulative incidence function, cardiovascular disease data of the Is...