Results 171 to 180 of about 3,477,506 (374)
Efficient Quantile Regression Analysis With Missing Observations
, 2015 Xuerong Chen, Alan T. K. Wan, Yong Zhou
openalex +1 more source
On the unit Burr-XII distribution with the quantile regression modeling and applications
Computational and Applied Mathematics, 2021 M. C. Korkmaz, C. Chesneau
semanticscholar +1 more source
Implementation of Machine Learning Models to Predict Functionality of Pea Flour From Its Composition
Cereal Chemistry, EarlyView.ABSTRACT Background and Objectives The goal of this research was to examine the relationship between the composition and functionality of pea flour using the following machine learning algorithms: linear regression, partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression, gradient‐boosted decision trees ...
Colten N. Nickerson +7 more
wiley +1 more source
Forecast of transmission line clearance using quantile regression‐based weather forecasts [PDF]
, 2022 Soheila Karimi +3 more
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Rank‐based estimation of propensity score weights via subclassification
Canadian Journal of Statistics, EarlyView.Abstract Propensity score (PS) weighting estimators are widely used for causal effect estimation and enjoy desirable theoretical properties, such as consistency and potential efficiency under correct model specification. However, their performance can degrade in practice due to sensitivity to PS model misspecification.
Linbo Wang +3 more
wiley +1 more source
Quantile Regression Analysis of Modifiable and Non-Modifiable Predictors of Stroke among Adults in South Africa [PDF]
, 2021 Delson Chikobvu, Lyness Matizirofa
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Canadian Journal of Statistics, EarlyView.Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne +2 more
wiley +1 more source
A goodness‐of‐fit test for regression models with discrete outcomes
Canadian Journal of Statistics, EarlyView.Abstract Regression models are often used to analyze discrete outcomes, but classical goodness‐of‐fit tests such as those based on the deviance or Pearson's statistic can be misleading or have little power in this context. To address this issue, we propose a new test, inspired by the work of Czado et al.
Lu Yang +2 more
wiley +1 more source