IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v87y2022i2d10.1007_s11336-021-09787-w.html
   My bibliography  Save this article

A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation

Author

Listed:
  • Steffen Nestler

    (University of Münster, Institut für Psychologie)

  • Sarah Humberg

    (University of Münster, Institut für Psychologie)

Abstract

Research in psychology is experiencing a rapid increase in the availability of intensive longitudinal data. To use such data for predicting feelings, beliefs, and behavior, recent methodological work suggested combinations of the longitudinal mixed-effect model with Lasso regression or with regression trees. The present article adds to this literature by suggesting an extension of these models that—in addition to a random effect for the mean level—also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After introducing the extended mixed-effect location scale (E-MELS), the extended mixed-effect location-scale Lasso model (Lasso E-MELS), and the extended mixed-effect location-scale tree model (E-MELS trees), we show how its parameters can be estimated using a marginal maximum likelihood approach. Using real and simulated example data, we illustrate how to use E-MELS, Lasso E-MELS, and E-MELS trees for building prediction models to forecast individuals’ daily nervousness. The article is accompanied by an R package (called mels) and functions that support users in the application of the suggested models.

Suggested Citation

  • Steffen Nestler & Sarah Humberg, 2022. "A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 506-532, June.
    • Handle: RePEc:spr:psycho:v:87:y:2022:i:2:d:10.1007_s11336-021-09787-w
    DOI: 10.1007/s11336-021-09787-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-021-09787-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11336-021-09787-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Donald Hedeker & Robin J. Mermelstein & Hakan Demirtas, 2008. "An Application of a Mixed-Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data," Biometrics, The International Biometric Society, vol. 64(2), pages 627-634, June.
    2. Fu, Wei & Simonoff, Jeffrey S., 2015. "Unbiased regression trees for longitudinal and clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 53-74.
    3. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521535380, January.
    4. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521828284, January.
    5. Ormerod, J. T. & Wand, M. P., 2010. "Explaining Variational Approximations," The American Statistician, American Statistical Association, vol. 64(2), pages 140-153.
    6. Anders Skrondal & Sophia Rabe‐Hesketh, 2009. "Prediction in multilevel generalized linear models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 659-687, June.
    7. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    8. Hajjem, Ahlem & Bellavance, François & Larocque, Denis, 2011. "Mixed effects regression trees for clustered data," Statistics & Probability Letters, Elsevier, vol. 81(4), pages 451-459, April.
    9. Hedeker, Donald & Nordgren, Rachel, 2013. "MIXREGLS: A Program for Mixed-Effects Location Scale Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i12).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sarfraz Hussain & Mohammad Enamul Hoque & Perengki Susanto & Waqas Ahmad Watto & Samina Haque & Pradeep Mishra, 2022. "The Quality of Fair Revaluation of Fixed Assets and Additional Calculations Aimed at Facilitating Prospective Investors’ Decisions," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    2. Messner, Wolfgang, 2024. "Exploring multilevel data with deep learning and XAI: The effect of personal-care advertising spending on subjective happiness," International Business Review, Elsevier, vol. 33(1).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2017. "A Variational Maximization–Maximization Algorithm for Generalized Linear Mixed Models with Crossed Random Effects," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 693-716, September.
    2. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2014. "Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data," European Journal of Operational Research, Elsevier, vol. 233(3), pages 749-758.
    3. Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.
    4. Rodrigo V. Ventura & Manoela Cabo & Rafael Caixeta & Elton Fernandes & Vicente Aprigliano Fernandes, 2020. "Air Transportation Income and Price Elasticities in Remote Areas: The Case of the Brazilian Amazon Region," Sustainability, MDPI, vol. 12(15), pages 1-18, July.
    5. Lynn, Peter & Bosch, Oriol, 2021. "Methodological lessons from the pilot longitudinal survey on debt advice," ISER Working Paper Series 2021-03, Institute for Social and Economic Research.
    6. Caudill, Jonathan W. & Getty, Ryan & Smith, Rick & Patten, Ryan & Trulson, Chad R., 2013. "Discouraging window breakers: The lagged effects of police activity on crime," Journal of Criminal Justice, Elsevier, vol. 41(1), pages 18-23.
    7. Maliyamu Abudureheman & Qingzhe Jiang & Xiucheng Dong & Cong Dong, 2022. "CO 2 Emissions in China: Does the Energy Rebound Matter?," Energies, MDPI, vol. 15(12), pages 1-25, June.
    8. Edward Frees & Jee-Seon Kim, 2006. "Multilevel Model Prediction," Psychometrika, Springer;The Psychometric Society, vol. 71(1), pages 79-104, March.
    9. Kaixing Huang, 2015. "The Economic Impacts of Global Warming on Agriculture: the Role of Adaptation," School of Economics and Public Policy Working Papers 2015-20, University of Adelaide, School of Economics and Public Policy.
    10. Torben Dall Schmidt & Peter Sandholt Jensen, 2012. "Social networks and regional recruitment of foreign labour: Firm recruitment methods and spatial sorting in Denmark," Papers in Regional Science, Wiley Blackwell, vol. 91(4), pages 795-821, November.
    11. Alberto Aiolfi & Emanuele Asti & Emanuele Rausa & Giulia Bonavina & Gianluca Bonitta & Luigi Bonavina, 2018. "Use of C-reactive protein for the early prediction of anastomotic leak after esophagectomy: Systematic review and Bayesian meta-analysis," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-13, December.
    12. Giulio Fusco, 2022. "Climate Change and Food Security in the Northern and Eastern African Regions: A Panel Data Analysis," Sustainability, MDPI, vol. 14(19), pages 1-10, October.
    13. Zhang, Duo & Wang, Min, 2018. "Objective Bayesian inference for the intraclass correlation coefficient in linear models," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 292-296.
    14. Antonio Ruiz Porras, 2016. "La investigación econométrica mediante paneles de datos:historia, modelos y usos en México," Archivos Revista Economía y Política., Facultad de Ciencias Económicas y Administrativas, Universidad de Cuenca., vol. 24, pages 11-32, Julio.
    15. Robert A. Kingwara, 2020. "Corporate Social Responsibility Disclosure and Financial Performance of Firms in Kenya: A Stakeholder Approach," Business and Economic Research, Macrothink Institute, vol. 10(3), pages 90-115, September.
    16. Migbaru Alamirew Workneh & Zerayehu Sime Eshete, 2021. "Household Level Non-Monetary Poverty in Ethiopia and its Driving Factors: a Multidimensional Approach with Panel Estimation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(1), pages 145-168, February.
    17. Vanja Grozdić & Branislav Marić & Mladen Radišić & Jarmila Šebestová & Marcin Lis, 2020. "Capital Investments and Manufacturing Firms’ Performance: Panel-Data Analysis," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    18. Tobin Im & Wonhyuk Cho & Gregory Porumbescu, 2011. "An Empirical Analysis of the Relation Between Social Spending and Economic Growth in Developing Countries and OECD Members," Asia Pacific Journal of Public Administration, Taylor & Francis Journals, vol. 33(1), pages 37-55, June.
    19. Jesús F Rosel & Pilar Jara & Francisco H Machancoses & Jacinto Pallarés & Pedro Torrente & Sara Puchol & Juan J Canales, 2019. "Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-17, January.
    20. Lili Pi & Julian Lowe, 2011. "Can a powerful CEO avoid involuntary replacement?—An empirical study from China," Asia Pacific Journal of Management, Springer, vol. 28(4), pages 775-805, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:psycho:v:87:y:2022:i:2:d:10.1007_s11336-021-09787-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.