Results 61 to 70 of about 3,032,172 (354)

Quantile ratio regression

Statistics and Computing
AbstractWe introduce quantile ratio regression. Our proposed model assumes that the ratio of two arbitrary quantiles of a continuous response distribution is a function of a linear predictor. Thanks to basic quantile properties, estimation can be carried out on the scale of either the response or the link function.
Alessio Farcomeni, Marco Geraci
openaire   +4 more sources

Tumor‐stromal crosstalk and macrophage enrichment are associated with chemotherapy response in bladder cancer

FEBS Open Bio, EarlyView.
Chemoresistance in bladder cancer: Macrophage recruitment associated with CXCL1, CXCL5 and CXCL8 expression is characteristic of Gemcitabine/Cisplatin (Gem/Cis) Non‐Responder tumors (right side) while Responder tumors did not show substantial tumor‐stromal crosstalk (left side). All biological icons are attributed to Bioicons: carcinoma, cancerous‐cell‐
Sophie Leypold, Janik Riese, Lancelot Seillier, Mark Kühnel, Julia Pannhausen, Charlotte O. J. Fröhlich, Christian Martin, Peter Boor, Matthias Saar, Danny D. Jonigk, Nadine T. Gaisa, Michael Rose   +11 more
wiley   +1 more source

The Impact of Tilburg Frailty on Poststroke Fatigue in First‐Ever Stroke Patients: A Cross‐Sectional Study With Unified Measurement Tools and Improved Statistics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Poststroke fatigue (PSF) and frailty share substantial overlap in their manifestations, yet previous research has yielded conflicting results due to the use of heterogeneous frailty assessment tools. Objective To evaluate the independent impact of frailty on PSF using a unified measurement system (Tilburg Frailty Indicator, TFI ...
Chuan‐Bang Chen, Xiao‐Xue Wang, Shao‐Rui Bao, Sui‐Li Lin, Mei‐Chun Shu, Xi‐Xi Ye, Wen‐Jie Cong   +6 more
wiley   +1 more source

Prediction of composite indicators using locally weighted quantile regression

Nonlinear Analysis, 2018
The main goal of this paper is to improve the existing methods and tools used for solving penalized quantile regression problems. We modified the quantile regression method by implementing the extreme learning machine (ELM) algorithm and features of ...
Jurga Rukšenaitė, Pranas Vaitkus, Povilas Asijavičius   +2 more
doaj   +1 more source

Handling multicollinearity in quantile regression through the use of principal component regression

Metron, 2022
In many fields of applications, linear regression is the most widely used statistical method to analyze the effect of a set of explanatory variables on a response variable of interest. Classical least squares regression focuses on the conditional mean of
C. Davino, R. Romano, D. Vistocco
semanticscholar   +1 more source

Accelerated Progression of Gait Impairment in Parkinson's Disease and REM Sleep Without Atonia

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective People with Parkinson's disease (PD) and rapid eye movement (REM) sleep without atonia (RSWA) often have more severe gait disturbances compared to PD without RSWA. The association between the presence and expression of RSWA and the rate of progression of gait impairment in PD is unknown.
Sommer L. Amundsen‐Huffmaster, Jae Woo Chung, Maria E. Linn‐Evans, Matthew N. Petrucci, Chiahao Lu, Destiny J. Weaver, Joshua De Kam, Scott E. Cooper, Paul J. Tuite, Aleksandar Videnovic, Michael J. Howell, Colum D. MacKinnon   +11 more
wiley   +1 more source

Empirical likelihood for quantile regression models with response data missing at random

Open Mathematics, 2017
This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression model with response data missing at random.
Luo S., Pang Shuxia
doaj   +1 more source

EMPIRICAL REGRESSION QUANTILE

Journal of the Operations Research Society of Japan, 1991
Summary: This study proposes a new use of goal programming for empirically estimating a regression quantile hyperplane. The approach can yield regression quantile estimates that are less sensitive to not only non- Gaussian error distributions but also a small sample size than conventional regression quantile methods.
openaire   +2 more sources

Frailty Exacerbates Disability in Progressive Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background To evaluate frailty in severe progressive multiple sclerosis (PMS) and to investigate the underlying mechanisms. Methods This prospective, cross‐sectional, multicenter study enrolled a late severe PMS group requiring skilled nursing (n = 53) and an age, sex, and disease duration‐matched control PMS group (n = 53).
Taylor R. Wicks, Anna Wolska, Diala Ghazal, Dejan Jakimovski, Bianca Weinstock‐Guttman, Alexander Burnham, Niels Bergsland, Michael G. Dwyer, Alan T. Remaley, Robert Zivadinov, Murali Ramanathan   +10 more
wiley   +1 more source

Simulation Study The Implementation of Quantile Bootstrap Method on Autocorrelated Error

Cauchy: Jurnal Matematika Murni dan Aplikasi, 2018
Quantile regression is a regression method with the approach of separating or dividing data into certain quantiles by minimizing the number of absolute values from asymmetrical errors to overcome unfulfilled assumptions, including the presence of ...
Ovi Delviyanti Saputri, Ferra Yanuar, Dodi Devianto   +2 more
doaj   +1 more source