In this paper, a one-sample point predictor of the random variable X is studied. X is the occurrence of an event in any successive visits $L_i$ and $R_i$ :i=1,2…,n (interval censoring). Our proposed method is based on finding the expected value of the conditional distribution of X given $L_i$ and $R_i$ (i=1,2…,n). To make the desired prediction, our approach is on the basis of approximating the...

Abstaract. A novel simulation approach, called Adaptive Linked Importance Sampling (ALIS), is proposed to compute small failure probabilities encountered in high-dimensional reliability analysis of engineering systems. It was shown by Au and Beck (2003) that Importance Sampling (IS) does generally not work in high dimensions. A geometric understanding of why this is true when one uses a fixed i...

Inference for Dirichlet process hierarchical models is typically performed using Markov chain Monte Carlo methods, which can be roughly categorised into marginal and conditional methods. The former integrate out analytically the infinite-dimensional component of the hierarchical model and sample from the marginal distribution of the remaining variables using the Gibbs sampler. Conditional metho...

Via so-called constrained stochastic simulation gusts can be generated which satisfy some specified constraint. Constrained stochastic simulation is based on conditional densities of normal random variables and it has previously been applied to generate maximum amplitude gusts and velocity jumps. In this paper it is used in order to generate specific wind gusts which will lead to local maxima i...

BACKGROUND Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whe...

We study the problem of independence and conditional independence tests between categorical covariates and a continuous response variable, which has an immediate application in genetics. Instead of estimating the conditional distribution of the response given values of covariates, we model the conditional distribution of covariates given the discretized response (aka “slices”). By assigning a p...

In this paper, we study adaptive nonparametric regression estimation in the presence of conditional heteroskedastic error terms. We demonstrate that both the conditional mean and conditional variance functions in a nonparametric regression model can be estimated adaptively based on the local profile likelihood principle. Both the one-step Newton-Raphson estimator and the local profile likelihoo...

The concept of causality is naturally defined in terms of conditional distribution, however almost all the empirical works focus on causality in mean. This paper aim to propose a nonparametric statistic to test the conditional independence and Granger non-causality between two variables conditionally on another one. The test statistic is based on the comparison of conditional distribution funct...

The conditional filtering method is proposed as a subfilter combustion model for large-eddy simulation LES of turbulent nonpremixed combustion. The novel method is based on conditional filtering of a reactive scalar field and an extension of conditional moment closure CMC for LES. Filtering conditioned on isosurfaces of the mixture fraction is adopted to resolve small-scale mixing and chemical ...

Atmospheric carbon dioxide concentration (ACDC) is a crucial variable for many environmental simulation models, and is regarded as an important factor for predicting temperature and climate changes. However, the conditional variance of ACDC levels has not previously been examined. This paper analyses the trends and volatility in ACDC levels using monthly data from January 1965 to December 2002....