Results 111 to 120 of about 177,424 (286)
Quantum Algorithm for Spectral Regression for Regularized Subspace Learning
In this paper, we propose an efficient quantum algorithm for spectral regression which is a dimensionality reduction framework based on the regression and spectral graph analysis. The quantum algorithm involves two core subroutines: the quantum principal
Fan-Xu Meng +3 more
doaj +1 more source
Nonlinear Generalized Ridge Regression
A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relationship between a y-outcome variable and each of p = 2 or more potential x-predictor variables. The y-outcome is then predicted from all p of these "linearized" spline-predictors using the form of Generalized Ridge Regression that is most likely to yield ...
openaire +2 more sources
Beyond the Ban—Shedding Light on Smallholders' Price Vulnerability in Indonesia's Palm Oil Industry
ABSTRACT The Indonesian government imposed a palm oil export ban in April 2022 to address rising cooking oil prices. This study explores oil palm smallholders' vulnerability to the policy using descriptive statistics, Lasso, and post‐Lasso OLS regressions.
Charlotte‐Elena Reich +3 more
wiley +1 more source
Weighted Kernel Ridge Regression to Improve Genomic Prediction
Nonparametric models have recently been receiving increased attention due to their effectiveness in genomic prediction for complex traits. However, regular nonparametric models cannot effectively differentiate the relative importance of various SNPs ...
Chenguang Diao +6 more
doaj +1 more source
A Note on The Moments of Stochastic Shrinkage Parameters in Ridge Regression [PDF]
A common problem in econometric models and multiple regression in general is multicollinearity, which produces undesirable effects on the Least Squares estimators.
Hernán Rubio, Luis Firinguetti
core
ESTIMASI PARAMETER REGRESI RIDGE MENGGUNAKAN ITERASI HOERL, KENNARD, DAN BALDWIN (HKB) UNTUK PENANGANAN MULTIKOLINIERITAS (Studi Kasus Pengaruh BI Rate, Jumlah Uang Beredar, dan Nilai Tukar Rupiah terhadap Tingkat Inflasi di Indonesia) [PDF]
Regression analysis is statistical method used to analyze the dependence of respond variables to predictor variable. In multiple linear regression analysis, there are assumptions that must be met, they are normality, homoscedasticity, absence of ...
Solekakh, Nur Aeniatus
core
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang +5 more
wiley +1 more source
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley +1 more source
The Challenge of Handling Structured Missingness in Integrated Data Sources
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source
Nonparametric Generalized Ridge Regression
The title of this paper is potentially misleading, its methods are neither innovative nor easy to apply, and its numerical example is nearly ...
openaire +2 more sources

