Results 31 to 40 of about 15,278,209 (298)

Identification of organic compounds using artificial neural networks and refractive index [PDF]

open access: yesJournal of the Serbian Chemical Society, 2023
Identification of chemical compounds has many applications in science and technology. However, this process still relies significantly on the knowledge and experience of chemists.
Kirigiti Innocent Abel   +2 more
doaj   +1 more source

Sparse single-index model

open access: yesJ. Mach. Learn. Res., 2011
Let $(\bX, Y)$ be a random pair taking values in $\mathbb R^p \times \mathbb R$. In the so-called single-index model, one has $Y=f^{\star}(θ^{\star T}\bX)+\bW$, where $f^{\star}$ is an unknown univariate measurable function, $θ^{\star}$ is an unknown vector in $\mathbb R^d$, and $W$ denotes a random noise satisfying $\mathbb E[\bW|\bX]=0$.
Alquier, Pierre, Biau, Gérard
openaire   +5 more sources

A bootstrap test for single index models [PDF]

open access: yesStatistics, 2001
Single index models are frequently used in econometrics and biometrics. Logit and Probit models arc special cases with fixed link functions. In this paper we consider a bootstrap specification test that detects nonparametric deviations of the link function.
Härdle, Wolfgang   +2 more
openaire   +6 more sources

Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study

open access: yesRemote Sensing, 2021
Wheat yellow rust has a severe impact on wheat production and threatens food security in China; as such, an effective monitoring method is necessary at the regional scale.
Qiong Zheng   +8 more
doaj   +1 more source

On Single-Index Models beyond Gaussian Data

open access: yesAdvances in Neural Information Processing Systems 36, 2023
Sparse high-dimensional functions have arisen as a rich framework to study the behavior of gradient-descent methods using shallow neural networks, showcasing their ability to perform feature learning beyond linear models. Amongst those functions, the simplest are single-index models $f(x) = ϕ( x \cdot θ^*)$, where the labels are generated by an ...
Aaron Zweig   +2 more
openaire   +3 more sources

Simplified Pediatric Index of Mortality 3 Score by Explainable Machine Learning Algorithm

open access: yesCritical Care Explorations, 2021
OBJECTIVES:. Pediatric Index of Mortality 3 is a validated tool including 11 variables for the assessment of mortality risk in PICU patients. With the recent advances in explainable machine learning algorithms, we aimed to assess feasibility of ...
Orkun Baloglu, MD   +4 more
doaj   +1 more source

A single-index model with multiple-links

open access: yesJournal of Statistical Planning and Inference, 2020
In a regression model for treatment outcome in a randomized clinical trial, a treatment effect modifier is a covariate that has an interaction with the treatment variable, implying that the treatment efficacies vary across values of such a covariate.
Park, Hyung   +3 more
openaire   +3 more sources

Estimation of Single-Index Models Based on Boosting Techniques [PDF]

open access: yes, 2008
In single-index models the link or response function is not considered as fixed. The data determine the form of the unknown link function. In order to obtain a flexible form of the link function we specify the link function as an expansion in basis ...
Leitenstorfer, Florian, Tutz, Gerhard
core   +1 more source

Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma

open access: yesScientific Reports, 2022
Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on ...
Xiao Liu   +5 more
doaj   +1 more source

Score Tests for the Single Index Model [PDF]

open access: yesTechnometrics, 2002
The single index model is a generalization of the linear regression model with E(y|x) = g(x′β), where g is an unknown function. The model provides a flexible alternative to the linear regression model while providing more structure than a fully nonparametric approach.
Jeffrey S. Simonoff, Chih-Ling Tsai
openaire   +1 more source

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