Abstract This paper presents a novel approach using multiple linear regression to process transient signals from silicon photomultipliers. The method provides excellent noise suppression and pulse detection in scenarios with high count rate superimposed pulses. Insights into its implementation benchmark results are presented. We also show how this can be used automatically detect the shape give...

We propose a multiple imputation estimator for parameter estimation in a quantile regression model when some covariates are missing at random. The estimation procedure fully utilizes the entire dataset to achieve increased efficiency, and the resulting coefficient estimators are root-n consistent and asymptotically normal. To protect against possible model misspecification, we further propose a...

This paper proposes a new class of hidden Markov model (HMM) called multiple-regression HMM (MRHMM) that utilizes auxiliary features such as fundamental frequency ( ) and speaking styles that affect spectral parameters to better model the acoustic features of phonemes. Though such auxiliary features are considered to be the factors that degrade the performance of speech recognizers, the propose...

In this paper, we propose an iterative algorithm for multiple regression with fuzzy variables.While using the standard least-squares criterion as a performance index, we pose the regression problem as a gradient-descent optimisation. The separation of the evaluation of the gradient and the update of the regression variables makes it possible to avoid undue complication of analytical formulae fo...

The isotonic regression for two or more independent variables is a classic problem in data analysis. The classical solution involves enumeration of upper sets, which is computationally prohibitive unless the sample size is small. Here it is shown that the solution may be obtained through a single projection onto a convex polyhedral cone. The cone formulation allows an exact test of the null hyp...