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1
New restricted biased estimator based on modified unbiased ridge regression estimator
New restricted biased estimator based on modified unbiased ridge regression estimator
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New restricted biased estimator based on modified unbiased ridge regression estimator

Journal of physics. Conference series, 2021-05, Vol.1879 (3), p.32038 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1879/3/032038

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2
Applied regression analysis
Applied regression analysis
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Applied regression analysis

ISBN: 9780471170822 ;ISBN: 0471170828 ;EISBN: 1118625625 ;EISBN: 9781118625620 ;EISBN: 1118625684 ;EISBN: 9781118625682 ;DOI: 10.1002/9781118625590

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3
Regression: Models, Methods and Applications
Regression: Models, Methods and Applications
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Regression: Models, Methods and Applications

Springer-Verlag Berlin Heidelberg 2013 ;ISBN: 9783642343339 ;ISBN: 3642343333 ;ISBN: 3642343325 ;ISBN: 9783642343322 ;EISBN: 9783642343339 ;EISBN: 3642343333 ;DOI: 10.1007/978-3-642-34333-9 ;OCLC: 845247477 ;OCLC: 843758031

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4
Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression
Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression
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Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2010, Vol.72 (1), p.49-69 [Peer Reviewed Journal]

Copyright 2010 The Royal Statistical Society and Blackwell Publishing Ltd. ;2010 Royal Statistical Society ;2015 INIST-CNRS ;2010 The Royal Statistical Society and Blackwell Publishing Ltd ;ISSN: 1369-7412 ;EISSN: 1467-9868 ;DOI: 10.1111/j.1467-9868.2009.00725.x

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5
Points of Significance: Multiple linear regression
Points of Significance: Multiple linear regression
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Points of Significance: Multiple linear regression

Nature methods, 2015-12, Vol.12 (12), p.1103 [Peer Reviewed Journal]

Copyright Nature Publishing Group Dec 2015 ;ISSN: 1548-7091 ;EISSN: 1548-7105 ;DOI: 10.1038/nmeth.3665

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6
COORDINATE DESCENT ALGORITHMS FOR NONCONVEX PENALIZED REGRESSION, WITH APPLICATIONS TO BIOLOGICAL FEATURE SELECTION
COORDINATE DESCENT ALGORITHMS FOR NONCONVEX PENALIZED REGRESSION, WITH APPLICATIONS TO BIOLOGICAL FEATURE SELECTION
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COORDINATE DESCENT ALGORITHMS FOR NONCONVEX PENALIZED REGRESSION, WITH APPLICATIONS TO BIOLOGICAL FEATURE SELECTION

The annals of applied statistics, 2011-03, Vol.5 (1), p.232-253 [Peer Reviewed Journal]

2015 INIST-CNRS ;Copyright 2011 Institute of Mathematical Statistics ;Institute of Mathematical Statistics, 2011 2011 ;ISSN: 1932-6157 ;EISSN: 1941-7330 ;DOI: 10.1214/10-AOAS388 ;PMID: 22081779

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7
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
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The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

PeerJ. Computer science, 2021-07, Vol.7, p.e623-e623, Article e623 [Peer Reviewed Journal]

COPYRIGHT 2021 PeerJ. Ltd. ;2021 Chicco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 Chicco et al. 2021 Chicco et al. ;ISSN: 2376-5992 ;EISSN: 2376-5992 ;DOI: 10.7717/peerj-cs.623 ;PMID: 34307865

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8
Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia
Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia
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Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

Landslides, 2016-10, Vol.13 (5), p.839-856 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2015 ;Springer-Verlag Berlin Heidelberg 2016 ;ISSN: 1612-510X ;EISSN: 1612-5118 ;DOI: 10.1007/s10346-015-0614-1

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9
Quantile Regression in the Study of Developmental Sciences
Quantile Regression in the Study of Developmental Sciences
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Quantile Regression in the Study of Developmental Sciences

Child development, 2014-05, Vol.85 (3), p.861-881 [Peer Reviewed Journal]

Child Development © 2014 Society for Research in Child Development, Inc. ;2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc. ;2015 INIST-CNRS ;Copyright Blackwell Publishing Ltd. May-Jun 2014 ;Child Development © 2014 The Society for Research in Child Development, Inc. ;2013 The Authors Child Development © 2013 Society for Research in Child Development, Inc. All rights reserved. 2013 ;ISSN: 0009-3920 ;EISSN: 1467-8624 ;DOI: 10.1111/cdev.12190 ;PMID: 24329596 ;CODEN: CHDEAW

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10
Standards for Standardized Logistic Regression Coefficients
Standards for Standardized Logistic Regression Coefficients
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Standards for Standardized Logistic Regression Coefficients

Social forces, 2011-06, Vol.89 (4), p.1409-1428 [Peer Reviewed Journal]

The University of North Carolina ;The University of North Carolina 2011 ;Copyright © 2008 The University of North Carolina Press. ;2015 INIST-CNRS ;COPYRIGHT 2011 Oxford University Press ;COPYRIGHT 2011 Oxford University Press ;Copyright University of North Carolina Press Jun 2011 ;ISSN: 0037-7732 ;EISSN: 1534-7605 ;DOI: 10.1093/sf/89.4.1409 ;CODEN: SOFOAP

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11
Modelling of infiltration of sandy soil using gaussian process regression
Modelling of infiltration of sandy soil using gaussian process regression
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Modelling of infiltration of sandy soil using gaussian process regression

Modeling earth systems and environment, 2017-09, Vol.3 (3), p.1091-1100 [Peer Reviewed Journal]

Springer International Publishing AG 2017 ;Copyright Springer Science & Business Media 2017 ;ISSN: 2363-6203 ;EISSN: 2363-6211 ;DOI: 10.1007/s40808-017-0357-1

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12
Handbook of Regression Analysis
Handbook of Regression Analysis
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Handbook of Regression Analysis

ISBN: 0470887168 ;ISBN: 9780470887165 ;ISBN: 1118532813 ;ISBN: 9781118532812 ;ISBN: 111853283X ;ISBN: 9781118532836 ;EISBN: 1118532813 ;EISBN: 9781118532812 ;EISBN: 111853283X ;EISBN: 9781118532836 ;DOI: 10.1002/9781118532843 ;OCLC: 823719348

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13
Confidence intervals for low dimensional parameters in high dimensional linear models
Confidence intervals for low dimensional parameters in high dimensional linear models
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Confidence intervals for low dimensional parameters in high dimensional linear models

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2014-01, Vol.76 (1), p.217-242 [Peer Reviewed Journal]

Copyright © 2014 The Royal Statistical Society and Blackwell Publishing Ltd. ;2013 Royal Statistical Society ;Copyright © 2014 The Royal Statistical Society and Blackwell Publishing Ltd ;ISSN: 1369-7412 ;EISSN: 1467-9868 ;DOI: 10.1111/rssb.12026

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14
Fifty Years of Classification and Regression Trees
Fifty Years of Classification and Regression Trees
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Fifty Years of Classification and Regression Trees

International statistical review, 2014-12, Vol.82 (3), p.329-348 [Peer Reviewed Journal]

2014 International Statistical Institute ;2014 The Authors. International Statistical Review © 2014 International Statistical Institute ;ISSN: 0306-7734 ;EISSN: 1751-5823 ;DOI: 10.1111/insr.12016

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15
Benign overfitting in linear regression
Benign overfitting in linear regression
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Benign overfitting in linear regression

Proceedings of the National Academy of Sciences - PNAS, 2020-12, Vol.117 (48), p.30063-30070 [Peer Reviewed Journal]

Copyright National Academy of Sciences Dec 1, 2020 ;2020 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1907378117 ;PMID: 32332161

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16
Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia
Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia
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Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia

Ain Shams Engineering Journal, 2021-06, Vol.12 (2), p.1651-1663 [Peer Reviewed Journal]

ISSN: 2090-4479 ;DOI: 10.1016/j.asej.2020.09.011

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17
Linear regression analysis: theory and computing.
Linear regression analysis: theory and computing.
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Linear regression analysis: theory and computing.

ISBN: 9789812834102 ;ISBN: 9812834109 ;ISBN: 9812834117 ;ISBN: 9789812834119 ;EISBN: 9812834117 ;EISBN: 9789812834119 ;DOI: 10.1142/6986 ;LCCallNum: QA278.2

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18
Optimal subsampling for composite quantile regression model in massive data
Optimal subsampling for composite quantile regression model in massive data
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Article 		Add to My Research

Optimal subsampling for composite quantile regression model in massive data

Statistical papers (Berlin, Germany), 2022-08, Vol.63 (4), p.1139-1161 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. ;ISSN: 0932-5026 ;EISSN: 1613-9798 ;DOI: 10.1007/s00362-021-01271-y

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19
How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice
How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice
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How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice

Political analysis, 2019-04, Vol.27 (2), p.163-192 [Peer Reviewed Journal]

Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. ;The Author(s) 2018 ;2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. This article is published under (http://creativecommons.org/licenses/by-nc-sa/4.0/) (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1047-1987 ;EISSN: 1476-4989 ;DOI: 10.1017/pan.2018.46

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20
Estimation of irrigation water quality index with development of an optimum model: a case study
Estimation of irrigation water quality index with development of an optimum model: a case study
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Article 		Add to My Research

Estimation of irrigation water quality index with development of an optimum model: a case study

Environment, development and sustainability, 2020-06, Vol.22 (5), p.4771-4786 [Peer Reviewed Journal]

Springer Nature B.V. 2019 ;Springer Nature B.V. 2019. ;ISSN: 1387-585X ;EISSN: 1573-2975 ;DOI: 10.1007/s10668-019-00405-5

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