Learning Survival Distributions with the Asymmetric Laplace Distribution. [PDF]
Proc Mach Learn ResProbabilistic survival analysis models seek to estimate the distribution of the future occurrence (time) of an event given a set of covariates. In recent years, these models have preferred nonparametric specifications that avoid directly estimating ...
Sheng D, Henao R.
europepmc +7 more sources
Asymmetric Laplace Distribution Models for Financial Data: VaR and CVaR
Symmetry, 2022 In the field of financial risk measurement, Asymmetric Laplace (AL) laws are used. The assumption of normalcy is used in traditional approaches for calculating financial risk.
Huiting Jing
exaly +3 more sources
Journal of Probability and Statistics, 2019 In this paper, we are interested in estimating several quantiles simultaneously in a regression context via the Bayesian approach. Assuming that the error term has an asymmetric Laplace distribution and using the relation between two distinct quantiles ...
Josephine Merhi Bleik
doaj +4 more sources
Robust LPV models identification approach based on shifted asymmetric Laplace distribution
Measurement + Control, 2021 This paper focuses on the robust parameters estimation algorithm of linear parameters varying (LPV) models. The classical robust identification techniques deal with the polluted training data, for example, outliers in white noise.
Chao Xu, Xianqiang Yang, Miao Yu
doaj +2 more sources
A latent class based imputation method under Bayesian quantile regression framework using asymmetric Laplace distribution for longitudinal medication usage data with intermittent missing values. [PDF]
J Biopharm Stat, 2020 Evaluating the association between diseases and the longitudinal pattern of pharmacological therapy has become increasingly important. However, in many longitudinal studies, self-reported medication usage data collected at patients’ follow-up visits ...
Lee M +5 more
europepmc +2 more sources
Quantile regression for longitudinal data using the asymmetric Laplace distribution. [PDF]
Biostatistics, 2007 In longitudinal studies, measurements of the same individuals are taken repeatedly through time. Often, the primary goal is to characterize the change in response over time and the factors that influence change. Factors can affect not only the location but also more generally the shape of the distribution of the response over time.
M. Geraci, M. Bottai
semanticscholar +3 more sources
Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions [PDF]
Computational Statistics and Data Analysis, 2019 Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one parameter controlling the proportion of
Katherine Morris +2 more
exaly +4 more sources
The multivariate asymmetric slash Laplace distribution and its applications
Statistica, 2013 We have introduced a multivariate asymmetric-slash Laplace distribution, a flexible distribution that can take skewness and heavy tails into account. This distribution is useful in simulation studies where it can introduce distributional challenges in ...
Bindu Punathumparambath
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Marshall-Olkin Generalized Asymmetric Laplace distributions and processes
Statistica, 2013 The Marshall-Olkin Generalised Asymmetric Laplace distribution is introduced and studied. An approximation is made and various properties including self decomposability, geometric infinite divisibility, limit properties etc.are established.
E. Krishna, Kanichukattu K. Jose
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Bayesian Regularized Quantile Regression Analysis Based on Asymmetric Laplace Distribution
Journal of Applied Mathematics and Physics, 2020 In recent years, variable selection based on penalty likelihood methods has aroused great concern. Based on the Gibbs sampling algorithm of asymmetric Laplace distribution, this paper considers the quantile regression with adaptive Lasso and Lasso ...
Qiaoqiao Tang, Haomin Zhang, Shi-ju Gong
semanticscholar +3 more sources