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A Penalized Likelihood Framework for High-Dimensional Phylogenetic Comparative Methods and an Application to New-World Monkeys Brain Evolution
A Penalized Likelihood Framework for High-Dimensional Phylogenetic Comparative Methods and an Application to New-World Monkeys Brain Evolution
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A Penalized Likelihood Framework for High-Dimensional Phylogenetic Comparative Methods and an Application to New-World Monkeys Brain Evolution

Systematic biology, 2019-01, Vol.68 (1), p.93-116 [Peer Reviewed Journal]

The Author(s) 2018 ;The Author(s) 2018. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com 2018 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1063-5157 ;EISSN: 1076-836X ;DOI: 10.1093/sysbio/syy045 ;PMID: 29931145

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2
Tuning parameter selection in high dimensional penalized likelihood
Tuning parameter selection in high dimensional penalized likelihood
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Tuning parameter selection in high dimensional penalized likelihood

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2013-06, Vol.75 (3), p.531-552 [Peer Reviewed Journal]

Copyright © 2013 The Royal Statistical Society and Blackwell Publishing Ltd. ;2012 Royal Statistical Society ;Copyright © 2012 The Royal Statistical Society and Blackwell Publishing Ltd ;ISSN: 1369-7412 ;EISSN: 1467-9868 ;DOI: 10.1111/rssb.12001

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3
Model Selection of Generalized Estimating Equation With Divergent Model Size
Model Selection of Generalized Estimating Equation With Divergent Model Size
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Model Selection of Generalized Estimating Equation With Divergent Model Size

Statistica Sinica, 2023 [Peer Reviewed Journal]

ISSN: 1017-0405 ;DOI: 10.5705/ss.202020.0197

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4
Joint Detection and Localization in Distributed MIMO Radars Employing Waveforms with Imperfect Auto- and Cross-Correlation
Joint Detection and Localization in Distributed MIMO Radars Employing Waveforms with Imperfect Auto- and Cross-Correlation
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Joint Detection and Localization in Distributed MIMO Radars Employing Waveforms with Imperfect Auto- and Cross-Correlation

IEEE transactions on vehicular technology, 2023-12, p.1-13 [Peer Reviewed Journal]

ISSN: 0018-9545 ;EISSN: 1939-9359 ;DOI: 10.1109/TVT.2023.3293023 ;CODEN: ITVTAB

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5
Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection
Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection
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Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection

Psychometrika, 2021-03, Vol.86 (1), p.65-95 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0033-3123 ;EISSN: 1860-0980 ;DOI: 10.1007/s11336-021-09751-8 ;PMID: 33768403

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6
Selection Consistency of Lasso-Based Procedures for Misspecified High-Dimensional Binary Model and Random Regressors
Selection Consistency of Lasso-Based Procedures for Misspecified High-Dimensional Binary Model and Random Regressors
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Selection Consistency of Lasso-Based Procedures for Misspecified High-Dimensional Binary Model and Random Regressors

Entropy (Basel, Switzerland), 2020-01, Vol.22 (2), p.153 [Peer Reviewed Journal]

2020 by the authors. 2020 ;ISSN: 1099-4300 ;EISSN: 1099-4300 ;DOI: 10.3390/e22020153 ;PMID: 33285928

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7
Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods
Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods
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Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods

ISBN: 9783036542973 ;ISBN: 3036542973 ;ISBN: 9783036542980 ;ISBN: 3036542981 ;DOI: 10.3390/books978-3-0365-4298-0

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8
Selection of NARX models estimated using weighted least squares method via GIC-based method and l1-norm regularization methods
Selection of NARX models estimated using weighted least squares method via GIC-based method and l1-norm regularization methods
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Selection of NARX models estimated using weighted least squares method via GIC-based method and l1-norm regularization methods

Nonlinear dynamics, 2012-11, Vol.70 (3), p.1831-1846 [Peer Reviewed Journal]

Springer Science+Business Media B.V. 2012 ;ISSN: 0924-090X ;EISSN: 1573-269X ;DOI: 10.1007/s11071-012-0576-y

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9
Random Subspace Method for high-dimensional regression with the R package regRSM
Random Subspace Method for high-dimensional regression with the R package regRSM
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Random Subspace Method for high-dimensional regression with the R package regRSM

Computational statistics, 2016-09, Vol.31 (3), p.943-972 [Peer Reviewed Journal]

The Author(s) 2016 ;Springer-Verlag Berlin Heidelberg 2016 ;ISSN: 0943-4062 ;EISSN: 1613-9658 ;DOI: 10.1007/s00180-016-0658-2

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10
Variable selection in linear models
Variable selection in linear models
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Variable selection in linear models

Wiley interdisciplinary reviews. Computational statistics, 2014-01, Vol.6 (1), p.1-9 [Peer Reviewed Journal]

2013 Wiley Periodicals, Inc. ;Copyright © 2014 Wiley Periodicals, Inc. ;ISSN: 1939-5108 ;EISSN: 1939-0068 ;DOI: 10.1002/wics.1284

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11
The ROPE framework for model selection in linear regression
The ROPE framework for model selection in linear regression
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The ROPE framework for model selection in linear regression

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12
On Model Selection Curves
On Model Selection Curves
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On Model Selection Curves

International statistical review, 2010-08, Vol.78 (2), p.240-256 [Peer Reviewed Journal]

2010 International Statistical Institute ;2010 The Authors. Journal compilation © 2010 International Statistical Institute ;2015 INIST-CNRS ;ISSN: 0306-7734 ;EISSN: 1751-5823 ;DOI: 10.1111/j.1751-5823.2010.00108.x ;CODEN: ISTRDP

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13
Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection
Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection
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Article 		Add to My Research

Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection

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14
Generalized Information Criteria in Model Selection for Locally Stationary Processes
Generalized Information Criteria in Model Selection for Locally Stationary Processes
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Generalized Information Criteria in Model Selection for Locally Stationary Processes

JOURNAL OF THE JAPAN STATISTICAL SOCIETY, 2008/07/16, Vol.38(1), pp.157-171

2008 Japan Statistical Society ;Copyright Japan Science and Technology Agency 2008 ;ISSN: 1882-2754 ;EISSN: 1348-6365 ;DOI: 10.14490/jjss.38.157

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15
Optimal tuning parameter estimation in maximum penalized likelihood method
Optimal tuning parameter estimation in maximum penalized likelihood method
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Optimal tuning parameter estimation in maximum penalized likelihood method

Annals of the Institute of Statistical Mathematics, 2010-06, Vol.62 (3), p.413-438 [Peer Reviewed Journal]

The Institute of Statistical Mathematics, Tokyo 2008 ;The Institute of Statistical Mathematics, Tokyo 2010 ;ISSN: 0020-3157 ;EISSN: 1572-9052 ;DOI: 10.1007/s10463-008-0186-0

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16
A MODEL FOR FIELD TOXICITY TESTS
A MODEL FOR FIELD TOXICITY TESTS
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A MODEL FOR FIELD TOXICITY TESTS

Environmetrics (London, Ont.), 1996-03, Vol.7 (2), p.215-229 [Peer Reviewed Journal]

Copyright © 1996 John Wiley & Sons, Ltd. ;1996 INIST-CNRS ;ISSN: 1180-4009 ;EISSN: 1099-095X ;DOI: 10.1002/(SICI)1099-095X(199603)7:2<215::AID-ENV206>3.0.CO;2-B ;CODEN: ENVCEE

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