IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i4p3431-3444.html
   My bibliography  Save this article

Flexible joint modeling of mean and dispersion for the directional tuning of neuronal spike counts

Author

Listed:
  • María Alonso‐Pena
  • Irène Gijbels
  • Rosa M. Crujeiras

Abstract

The study of how the number of spikes in a middle temporal visual area (MT/V5) neuron is tuned to the direction of a visual stimulus has attracted considerable attention over the years, but recent studies suggest that the variability of the number of spikes might also be influenced by the directional stimulus. This entails that Poisson regression models are not adequate for this type of data, as the observations usually present over/underdispersion (or both) with respect to the Poisson distribution. This paper makes use of the double exponential family and presents a flexible model to estimate, jointly, the mean and dispersion functions, accounting for the effect of a circular covariate. The empirical performance of the proposal is explored via simulations and an application to a neurological data set is shown.

Suggested Citation

  • María Alonso‐Pena & Irène Gijbels & Rosa M. Crujeiras, 2023. "Flexible joint modeling of mean and dispersion for the directional tuning of neuronal spike counts," Biometrics, The International Biometric Society, vol. 79(4), pages 3431-3444, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3431-3444
    DOI: 10.1111/biom.13882
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13882
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13882?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
    ---><---

    References listed on IDEAS

    as
    1. Robert E. Gaunt & Satish Iyengar & Adri B. Olde Daalhuis & Burcin Simsek, 2019. "An asymptotic expansion for the normalizing constant of the Conway–Maxwell–Poisson distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(1), pages 163-180, February.
    2. Y. Lee & J. A. Nelder, 2000. "The relationship between double‐exponential families and extended quasi‐likelihood families, with application to modelling Geissler's human sex ratio data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 413-419.
    3. I. Gijbels & I. Prosdocimi & G. Claeskens, 2010. "Nonparametric estimation of mean and dispersion functions in extended generalized linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 580-608, November.
    4. Galit Shmueli & Thomas P. Minka & Joseph B. Kadane & Sharad Borle & Peter Boatwright, 2005. "A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 127-142, January.
    5. Joseph A Lombardo & Matthew V Macellaio & Bing Liu & Stephanie E Palmer & Leslie C Osborne, 2018. "State dependence of stimulus-induced variability tuning in macaque MT," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-28, October.
    6. I. Gijbels & I. Prosdocimi, 2011. "Smooth estimation of mean and dispersion function in extended generalized additive models with application to Italian induced abortion data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2391-2411, December.
    7. Christophe Croux & Irène Gijbels & Ilaria Prosdocimi, 2012. "Robust Estimation of Mean and Dispersion Functions in Extended Generalized Additive Models," Biometrics, The International Biometric Society, vol. 68(1), pages 31-44, March.
    8. J. Fan & M. Farmen & I. Gijbels, 1998. "Local maximum likelihood estimation and inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 591-608.
    Full references (including those not matched with items on IDEAS)

    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. Morris, Darcy Steeg & Raim, Andrew M. & Sellers, Kimberly F., 2020. "A Conway–Maxwell-multinomial distribution for flexible modeling of clustered categorical data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    2. Christophe Croux & Irène Gijbels & Ilaria Prosdocimi, 2012. "Robust Estimation of Mean and Dispersion Functions in Extended Generalized Additive Models," Biometrics, The International Biometric Society, vol. 68(1), pages 31-44, March.
    3. Suryo Adi Rakhmawan & Tahir Mahmood & Nasir Abbas & Muhammad Riaz, 2024. "Unifying mortality forecasting model: an investigation of the COM–Poisson distribution in the GAS model for improved projections," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(4), pages 800-826, October.
    4. David M. Cutler & Adriana Lleras-Muney & Tom Vogl, 2008. "Socioeconomic Status and Health: Dimensions and Mechanisms," NBER Working Papers 14333, National Bureau of Economic Research, Inc.
    5. Xueying Zheng & Wing Fung & Zhongyi Zhu, 2013. "Robust estimation in joint mean–covariance regression model for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 617-638, August.
    6. J. Franke & J.-P. Stockis & J. Tadjuidje-Kamgaing & W. Li, 2011. "Mixtures of nonparametric autoregressions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 287-303.
    7. Sellers, Kimberly F. & Morris, Darcy Steeg & Balakrishnan, Narayanaswamy, 2016. "Bivariate Conway–Maxwell–Poisson distribution: Formulation, properties, and inference," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 152-168.
    8. Huaping Chen, 2023. "A New Soft-Clipping Discrete Beta GARCH Model and Its Application on Measles Infection," Stats, MDPI, vol. 6(1), pages 1-19, February.
    9. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    10. Wongsa-art, Pipat & Kim, Namhyun & Xia, Yingcun & Moscone, Francesco, 2024. "Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England," Regional Science and Urban Economics, Elsevier, vol. 106(C).
    11. Mevin B. Hooten & Michael R. Schwob & Devin S. Johnson & Jacob S. Ivan, 2023. "Multistage hierarchical capture–recapture models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    12. Can Zhou & Yan Jiao & Joan Browder, 2019. "How much do we know about seabird bycatch in pelagic longline fisheries? A simulation study on the potential bias caused by the usually unobserved portion of seabird bycatch," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    13. S. Hadi Khazraee & Antonio Jose Sáez‐Castillo & Srinivas Reddy Geedipally & Dominique Lord, 2015. "Application of the Hyper‐Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes," Risk Analysis, John Wiley & Sons, vol. 35(5), pages 919-930, May.
    14. Kimberly F. Sellers & Tong Li & Yixuan Wu & Narayanaswamy Balakrishnan, 2021. "A Flexible Multivariate Distribution for Correlated Count Data," Stats, MDPI, vol. 4(2), pages 1-19, April.
    15. Peng, Rong & Lu, Zudi, 2024. "Semiparametric Averaging of Nonlinear Marginal Logistic Regressions and Forecasting for Time Series Classification," Econometrics and Statistics, Elsevier, vol. 31(C), pages 19-37.
    16. Darcy Steeg Morris & Kimberly F. Sellers, 2022. "A Flexible Mixed Model for Clustered Count Data," Stats, MDPI, vol. 5(1), pages 1-18, January.
    17. Royce A. Francis & Srinivas Reddy Geedipally & Seth D. Guikema & Soma Sekhar Dhavala & Dominique Lord & Sarah LaRocca, 2012. "Characterizing the Performance of the Conway‐Maxwell Poisson Generalized Linear Model," Risk Analysis, John Wiley & Sons, vol. 32(1), pages 167-183, January.
    18. Linton, Oliver & Xiao, Zhijie, 2019. "Efficient estimation of nonparametric regression in the presence of dynamic heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(2), pages 608-631.
    19. Kohler, Michael & Krzyzak, Adam, 2007. "Asymptotic confidence intervals for Poisson regression," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 1072-1094, May.
    20. Joseph B. Kadane & Ramayya Krishnan & Galit Shmueli, 2006. "A Data Disclosure Policy for Count Data Based on the COM-Poisson Distribution," Management Science, INFORMS, vol. 52(10), pages 1610-1617, October.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:79:y:2023:i:4:p:3431-3444. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.