Results 91 to 100 of about 442,883 (292)

Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models [PDF]

, 2010
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear.
Fan, Jianqing, Feng, Yang, Song, Rui
core   +1 more source

Response to Allopurinol and Febuxostat According to the Fractional Excretion of Urate in Men With Gout

Arthritis Care &Research, EarlyView.
Objective Body mass index (BMI), glomerular filtration rate (GFR), and pretreatment urate levels have been reported to influence the urate‐lowering response to allopurinol. We investigated whether the fractional excretion of uric acid (FEUA) also modulates this response and relates to oxypurinol concentrations.
Pascal Richette, Anouk Walter‐Petrich, Quang Dinh Nguyen, Matthieu Resche‐Rigon, Quang Huy Dinh Nguyen, Minh Duc Do, Thao Phuong Mai, Tuan Duc Nguyen, Fernando Perez‐Ruiz, Emmanuel Letavernier, Tristan Pascart, Augustin Latourte, Hang Korng Ea, Thomas Bardin   +13 more
wiley   +1 more source

Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships

Scientific Reports
The universality of the allometric model for describing the length–weight relationship in marine species has been questioned, particularly for some invertebrates such as sea urchins, clams, and barnacles.
Gorka Bidegain, Marta Sestelo, Patricia L. Luque, Eric N. Powell, Arantza Irirarte, Ibon Uriarte, Daphne Munroe   +6 more
doaj   +1 more source

The comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods. [PDF]

Sci Prog, 2021
Yurtcu M, Kelecioglu H, Boone EL.
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Construction of a geographically weighted nonparametric regression model fit test

MethodsX
One of the approach of Geographically Weighted Regression (GWR) models is the Geographically Weighted Nonparametric Regression (GWNR) has more parameters than the GWR model.
Lilis Laome, I Nyoman Budiantara, Vita Ratnasari   +2 more
doaj   +1 more source

Methodology for using a Bayesian nonparametric model to uncover universal patterns in color naming. [PDF]

MethodsX, 2021
Joe K, Gooyabadi M.
europepmc   +1 more source

Near‐Infrared Emitting Lanthanide Catecholate Giant Single Crystals – Morphology Control and Photon Down‐Conversion

Advanced Functional Materials, EarlyView.
Controlled syntheses of lanthanide coordination polymers based on the dihydroxybenzoquinone (DHBQ) organic linker afforded large single crystals of Ln‐DHBQ CPs (Ln = Yb, Nd). A novel structural variant of Yb‐DHBQ is identified by means of single crystal diffraction analysis.
Marina I. Schönherr, Alexander Biewald, Andre Mähringer, Thaddäus J. Koller, Peter Mayer, Markus Döblinger, Achim Hartschuh, Dana D. Medina   +7 more
wiley   +1 more source

A BAYESIAN NONPARAMETRIC MODEL FOR INFERRING SUBCLONAL POPULATIONS FROM STRUCTURED DNA SEQUENCING DATA. [PDF]

Ann Appl Stat, 2021
He S, Schein A, Sarsani V, Flaherty P.
europepmc   +1 more source

Nonparametric weighted stochastic block models [PDF]

Physical Review E, 2018
We present a Bayesian formulation of weighted stochastic block models that can be used to infer the large-scale modular structure of weighted networks, including their hierarchical organization. Our method is nonparametric, and thus does not require the prior knowledge of the number of groups or other dimensions of the model, which are instead inferred
openaire   +3 more sources