IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v74y2020i3p261-274.html
   My bibliography  Save this article

Introducing a general class of species diversification models for phylogenetic trees

Author

Listed:
  • Francisco Richter
  • Bart Haegeman
  • Rampal S. Etienne
  • Ernst C. Wit

Abstract

Phylogenetic trees are types of networks that describe the temporal relationship between individuals, species, or other units that are subject to evolutionary diversification. Many phylogenetic trees are constructed from molecular data that is often only available for extant species, and hence they lack all or some of the branches that did not make it into the present. This feature makes inference on the diversification process challenging. For relatively simple diversification models, analytical or numerical methods to compute the likelihood exist, but these do not work for more realistic models in which the likelihood depends on properties of the missing lineages. In this article, we study a general class of species diversification models, and we provide an expectation‐maximization framework in combination with a uniform sampling scheme to perform maximum likelihood estimation of the parameters of the diversification process.

Suggested Citation

  • Francisco Richter & Bart Haegeman & Rampal S. Etienne & Ernst C. Wit, 2020. "Introducing a general class of species diversification models for phylogenetic trees," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 261-274, August.
    • Handle: RePEc:bla:stanee:v:74:y:2020:i:3:p:261-274
    DOI: 10.1111/stan.12205
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/stan.12205
    Download Restriction: no
    File URL: https://libkey.io/10.1111/stan.12205?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. Ernst Wit & Edwin van den Heuvel & Jan-Willem Romeijn, 2012. "‘All models are wrong...’: an introduction to model uncertainty," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 217-236, August.
    2. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    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. Alston, C.L. & Mengersen, K.L. & Robert, C.P. & Thompson, J.M. & Littlefield, P.J. & Perry, D. & Ball, A.J., 2007. "Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4282-4296, May.
    2. Papastamoulis, Panagiotis & Iliopoulos, George, 2009. "Reversible Jump MCMC in mixtures of normal distributions with the same component means," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 900-911, February.
    3. Fisher, Mark & Jensen, Mark J., 2022. "Bayesian nonparametric learning of how skill is distributed across the mutual fund industry," Journal of Econometrics, Elsevier, vol. 230(1), pages 131-153.
    4. Lee, Sik-Yum & Song, Xin-Yuan, 2008. "On Bayesian estimation and model comparison of an integrated structural equation model," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4814-4827, June.
    5. Shuang Zhang & Xingdong Feng, 2022. "Distributed identification of heterogeneous treatment effects," Computational Statistics, Springer, vol. 37(1), pages 57-89, March.
    6. Fuentes-García, Ruth & Mena, Ramsés H. & Walker, Stephen G., 2019. "Modal posterior clustering motivated by Hopfield’s network," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 92-100.
    7. Li, Feng & Kang, Yanfei, 2018. "Improving forecasting performance using covariate-dependent copula models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 456-476.
    8. Panagiotis Papastamoulis & George Iliopoulos, 2013. "On the Convergence Rate of Random Permutation Sampler and ECR Algorithm in Missing Data Models," Methodology and Computing in Applied Probability, Springer, vol. 15(2), pages 293-304, June.
    9. Sik-Yum Lee, 2006. "Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 71(3), pages 541-564, September.
    10. Gustavo Alexis Sabillón & Luiz Gabriel Fernandes Cotrim & Daiane Aparecida Zuanetti, 2023. "A data-driven reversible jump for estimating a finite mixture of regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 350-369, March.
    11. Cai, Jing-Heng & Song, Xin-Yuan & Lam, Kwok-Hap & Ip, Edward Hak-Sing, 2011. "A mixture of generalized latent variable models for mixed mode and heterogeneous data," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2889-2907, November.
    12. Murasawa, Yasutomo, 2017. "Measuring the Distributions of Public Inflation Perceptions and Expectations in the UK," MPRA Paper 76244, University Library of Munich, Germany.
    13. N. T. Longford & Pierpaolo D'Urso, 2011. "Mixture models with an improper component," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2511-2521, January.
    14. Ungolo, Francesco & Kleinow, Torsten & Macdonald, Angus S., 2020. "A hierarchical model for the joint mortality analysis of pension scheme data with missing covariates," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 68-84.
    15. Ang Shan & Fengkai Yang, 2021. "Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm," Mathematics, MDPI, vol. 9(6), pages 1-13, March.
    16. Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
    17. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    18. Motegi, Ryosuke & Seki, Yoichi, 2023. "SMLSOM: The shrinking maximum likelihood self-organizing map," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    19. Park, Byung-Jung & Zhang, Yunlong & Lord, Dominique, 2010. "Bayesian mixture modeling approach to account for heterogeneity in speed data," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 662-673, June.
    20. Komárek, Arnost, 2009. "A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3932-3947, 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:stanee:v:74:y:2020:i:3:p:261-274. 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=0039-0402 .

    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.