A review of generalized linear mixed models
A review of the results on the Stein approach for estimators improvement.
Since 1956, a large number of papers have been devoted to Stein's technique of obtaining improved estimators of parameters, for several statistical models. We give a brief review of these papers, emphasizing those aspects which are interesting from the point of view of the theory of unbiased estimation.
A robust test for variance
A sampling scheme for reliability estimation.
A scalarization technique for computing the power and exponential moments of Gaussian random matrices.
A scale-space approach with wavelets to singularity estimation
This paper is concerned with the problem of determining the typical features of a curve when it is observed with noise. It has been shown that one can characterize the Lipschitz singularities of a signal by following the propagation across scales of the modulus maxima of its continuous wavelet transform. A nonparametric approach, based on appropriate thresholding of the empirical wavelet coefficients, is proposed to estimate the wavelet maxima of a signal observed with noise at various scales. In...
A scale-space approach with wavelets to singularity estimation
This paper is concerned with the problem of determining the typical features of a curve when it is observed with noise. It has been shown that one can characterize the Lipschitz singularities of a signal by following the propagation across scales of the modulus maxima of its continuous wavelet transform. A nonparametric approach, based on appropriate thresholding of the empirical wavelet coefficients, is proposed to estimate the wavelet maxima of a signal observed with noise at various scales....
A scratch removal method
We present a new type of scratch removal algorithm based on a causal adaptive multidimensional prediction. The predictor use available information from the failed pixel surrounding due to spectral and spatial correlation of multispectral data but not any information from failed pixel itself. Predictor parameters cannot be directly identified so a special approximation is introduced.
A second order approximation for the inverse of the distribution function of the sample mean
The classical quantile approximation for the sample mean, based on the central limit theorem, has been proved to fail when the sample size is small and we approach the tail of the distribution. In this paper we will develop a second order approximation formula for the quantile which improves the classical one under heavy tails underlying distributions, and performs very accurately in the upper tail of the distribution even for relatively small samples.
A Semiparametric Technique to Estimate Survival Probabilities.
A sensitivity analysis for causal parameters in structural proportional hazards models.
Deviations from assigned treatment occur often in clinical trials. In such a setting, the traditional intent-to-treat analysis does not measure biological efficacy but rather programmatic effectiveness. For all-or-nothing compliance situation, Loeys and Goetghebeur (2003) recently proposed a Structural Proportional Hazards method. It allows for casual estimation in the complier subpopulation provided the exclusion restriction holds: randomization per se has no effect unless exposure has changed....
A sequential Bayesian approach to estimating the dimension of a multinomial distribution
A sharp analysis on the asymptotic behavior of the Durbin–Watson statistic for the first-order autoregressive process
The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin–Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic normality for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated with the driven noise....
A short note on multivariate dependence modeling
As said by Mareš and Mesiar, necessity of aggregation of complex real inputs appears almost in any field dealing with observed (measured) real quantities (see the citation below). For aggregation of probability distributions Sklar designed his copulas as early as in 1959. But surprisingly, since that time only a very few literature have appeared dealing with possibility to aggregate several different pairwise dependencies into one multivariate copula. In the present paper this problem is tackled...
A short note on Perez’s approximation by dependence structure simplification
Perez’s approximations of probability distributions by dependence structure simplification were introduced in 1970s, much earlier than graphical Markov models. In this paper we will recall these Perez’s models, formalize the notion of a compatible system of elementary simplifications and show the necessary and sufficient conditions a system must fulfill to be compatible. For this we will utilize the apparatus of compositional models.
A Simple and Effective Scanning Rule for a Multi-Channel System.
A simple mathematical standardized measurement of acetabulum anteversion after total hip arthroplasty.
A Simple Method of Sampling without Replacement with Inclusion-Probabilities Exactly Proportional to Size .
A simple robust estimator of correlations for Gaussian stationary random sequences
A simple statistic to test generalized palindromic symmetry model in a 4x4 contingency table.
For a 4x4 contingency table, this note gives a simple statistic to test the goodness-of-fit of the generalized palindromic symmetry (GPS) model considered by McCullagh (1978). Also an asymptotic confidence interval for a parameter of interest in the GPS model is given. Two sets of unaided vision data are used as example.