Although Cox proportional hazards regression is the default analysis for time to event data, there is typically uncertainty about whether the effects of a predictor are more appropriately characterized by a multiplicative or additive model. To accommodate this uncertainty, we place a model selection prior on the coefficients in an additive-multiplicative hazards model. This prior assigns positi...

Spectral subtraction (SS) approach has been widely used for speech enhancement and recognition accuracy improvement, but becomes less effective when the additive noise is not white. In this paper, we propose to integrate wavelet transform and the SS algorithm. The spectrum of the additive noise in each frequency band obtained in this way can then be better approximated as white if the number of...

The family of k-additive measures has been introduced as a midterm between probabilities and general fuzzy measures and finds a wide number of applications in practice. However, its structure is different from other families of fuzzy measures and is certainly more complex (for instance, its vertices are not always {0, 1}valued), so it has not been yet fully studied. In this paper we present som...

random regression models (rrm) have become common for the analysis of longitudinal data or repeated records on individual over time. the goal of this paper was to explore the use of random regression models with orthogonal / legendre polynomials (rrl) to analyze new repeated measures called clutch size (cs) as a meristic trait for iranian native fowl. legendre polynomial functions of increasing...

Fast image search with efficient additive kernels and kernel locality-sensitive hashing has been proposed. As to hold the kernel functions, recent work has probed methods to create locality-sensitive hashing, which guarantee our approach's linear time; however existing methods still do not solve the problem of locality-sensitive hashing (LSH) algorithm and indirectly sacrifice the loss in accur...

A comprehensive survey of some recent results regarding parameterized inverse eigenvalue problems is given in this paper. Speciic topics include: additive and multiplicative inverse eigenvalue problems, classical inverse eigenvalue problems and generalized inverse eigenvalue problems. Both the theoretic and algorithmic aspects are reviewed. Some open problems are revealed to stimulate further r...

This paper presents a detailed numerical study on the performance of the standard phasing algorithms with random phase illumination (RPI). Phasing with high resolution RPI and the oversampling ratio σ=4 determines a unique phasing solution up to a global phase factor. Under this condition, the standard phasing algorithms converge rapidly to the true solution without stagnation. Excellent approx...

We devise a classification algorithm based on generalised linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the result...

We present a new method for function estimation and variable selection, specifically designed for additive models fitted by cubic splines. Our method involves regularizing additive models using the l1–norm, which generalizes Tibshirani’s lasso to the nonparametric setting. As in the linear case, it shrinks coefficients, some of them reducing exactly to zero. It gives parsimonious models, select...

This paper proposes a learning scheme based still image super-resolution reconstruction algorithm. Superresolution reconstruction is proposed as a binary classification problem and can be solved by conditional class probability estimation. Assuming the probability takes the form of additive logistic regression function, AdaBoost algorithm is used to predict the probability. Experiments on face ...