A Method of Variable Selection in Hayashi's Third Method of Quantification

Journal of the Japanese Society of Computational Statistics
doi 10.5183/jjscs1988.1.27
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Japanese Society of Computational Statistics

Related search

A Note on Variable Selection in Functional Regression via Random Subspace Method

Statistical Methods and Applications
UncertaintyStatisticsProbability
2018English

The mRMR Variable Selection Method: A Comparative Study for Functional Data

Journal of Statistical Computation and Simulation
StatisticsProbabilityApplied MathematicsUncertaintySimulationModeling
2015English

Regularized Least-Square Optimization Method for Variable Selection in Regression Models

Математички билтен/BULLETIN MATHÉMATIQUE DE LA SOCIÉTÉ DES MATHÉMATICIENS DE LA RÉPUBLIQUE MACÉDOINE
2017English

An Ensemble Variable Selection Method for Vibrational Spectroscopic Data Analysis

RSC Advances
ChemistryChemical Engineering
2019English

Regression With Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

PLoS ONE
Multidisciplinary
2012English

A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model

Complexity
MultidisciplinaryComputer Science
2018English

A New Method for Immunochemical Quantification

Biochemical Society Transactions
Biochemistry
1977English

Application of a Two-Step Third-Derivative Block Method for Starting Numerov Method

International Journal of Theoretical and Applied Mathematics
2017English

A Method for the Quantification of Leydig Cells in Man

Reproduction
Cell BiologyGynecologyEndocrinologyReproductive MedicineObstetricsEmbryology
1971English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy