Results 31 to 40 of about 392 (92)
Quantile regression in high-dimension with breaking [PDF]
, 2013 The paper considers a linear regression model in high-dimension for which the predictive variables can change the influence on the response variable at unknown times (called change-points).
Ciuperca, Gabriela
core +4 more sources
Journal of Mathematics, Volume 2025, Issue 1, 2025.This paper investigates the use of shrinkage estimators in the generalized Poisson hurdle (GPH) model for count data analysis. The GPH model effectively handles data with both excess zeros and over‐ or underdispersion. We propose shrinkage estimators to improve parameter estimation in this model and analyze their asymptotic properties, including biases
Hayder Hasan Rahmah Al-Gharrawi +3 more
wiley +1 more source
Valid causal inference with unobserved confounding in high-dimensional settings
Journal of Causal InferenceVarious methods have recently been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data-generating processes when high-dimensional nuisance models are estimated by post-model-selection or machine ...
Moosavi Niloofar +2 more
doaj +1 more source
Almost sure convergence for weighted sums of pairwise PQD random variables
, 2018 We obtain Marcinkiewicz-Zygmund strong laws of large numbers for weighted sums of pairwise positively quadrant dependent random variables stochastically dominated by a random variable $X \in \mathscr{L}_{p}$, $1 \leqslant p < 2$.
da Silva, João Lita
core +1 more source
Majalah Ilmiah Matematika dan StatistikaThis research intends to model high-dimensional data that contains multicollinearity in four machine-learning algorithms: Random Forest, K-Nearest Neighbor, XGBoost, and Regression Tree.
Nur Khamidah +3 more
doaj +1 more source
Selection of Tuning Parameters, Solution Paths and Standard Errors for Bayesian Lassos
, 2017 Penalized regression methods such as the lasso and elastic net (EN) have become popular for simultaneous variable selection and coefficient estimation. Implementation of these methods require selection of the penalty parameters.
Vivekananda Roy, S. Chakraborty
semanticscholar +1 more source
On the performance of the new minimax shrinkage estimators for a normal mean vector
Demonstratio MathematicaThis paper explores new classes of estimators for a multivariate normal mean (MNM) with an unknown variance and evaluating their performance based on the risk relative to the balanced loss function (BLF).
Benkhaled Abdelkader +3 more
doaj +1 more source
New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model
مجلة بغداد للعلوم, 2020 This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model.
Mustafa Ismaeel Naif Alheety
doaj
, 2017 Isotonic regression offers a flexible modeling approach under monotonicity assumptions, which are natural in many applications. Despite this attractive setting and extensive theoretical research, isotonic regression has enjoyed limited interest in ...
Ronny Luss, Saharon Rosset
semanticscholar +1 more source
, 2016 Understanding efficiency in high dimensional linear models is a longstanding problem of interest. Classical work with smaller dimensional problems dating back to Huber and Bickel has illustrated the clear benefits of efficient loss functions.
Jelena Bradic
semanticscholar +1 more source