MODELING AVIAN ABUNDANCE FROM REPLICATED COUNTS USING BINOMIAL MIXTURE MODELS

2005 ◽  
Vol 15 (4) ◽  
pp. 1450-1461 ◽  
Author(s):  
Marc Kéry ◽  
J. Andrew Royle ◽  
Hans Schmid
Keyword(s):  
Mixture Models ◽  
Avian Abundance ◽  
Binomial Mixture

scholarly journals Mixture models as a method for comparative sociality: social networks and demographic change in resident killer whales

2021 ◽  
Vol 75 (4) ◽  
Author(s):  
Samuel Ellis ◽  
Daniel W. Franks ◽  
Michael N. Weiss ◽  
Michael A. Cant ◽  
Paolo Domenici ◽  
...  
Keyword(s):  
Mixture Models ◽  
Demographic Change ◽  
Social Preferences ◽  
Social Bonds ◽  
Killer Whales ◽  
Animal Populations ◽  
Binomial Mixture ◽  
Opposite Sex ◽  
Social Associations ◽  
Social Association

Abstract In studies of social behaviour, social bonds are usually inferred from rates of interaction or association. This approach has revealed many important insights into the proximate formation and ultimate function of animal social structures. However, it remains challenging to compare social structure between systems or time-points because extrinsic factors, such as sampling methodology, can also influence the observed rate of association. As a consequence of these methodological challenges, it is difficult to analyse how patterns of social association change with demographic processes, such as the death of key social partners. Here we develop and illustrate the use of binomial mixture models to quantitatively compare patterns of social association between networks. We then use this method to investigate how patterns of social preferences in killer whales respond to demographic change. Resident killer whales are bisexually philopatric, and both sexes stay in close association with their mother in adulthood. We show that mothers and daughters show reduced social association after the birth of the daughter’s first offspring, but not after the birth of an offspring to the mother. We also show that whales whose mother is dead associate more with their opposite sex siblings and with their grandmother than whales whose mother is alive. Our work demonstrates the utility of using mixture models to compare social preferences between networks and between species. We also highlight other potential uses of this method such as to identify strong social bonds in animal populations. Significance statement Comparing patters of social associations between systems, or between the same systems at different times, is challenging due to the confounding effects of sampling and methodological differences. Here we present a method to allow social associations to be robustly classified and then compared between networks using binomial mixture models. We illustrate this method by showing how killer whales change their patterns of social association in response to the birth of calves and the death of their mother. We show that after the birth of her calf, females associate less with their mother. We also show that whales’ whose mother is dead associate more with their opposite sex siblings and grandmothers than whales’ whose mother is alive. This clearly demonstrates how this method can be used to examine fine scale temporal processes in animal social systems.

scholarly journals Partitioning Detectability Components in Populations Subject to Within-Season Temporary Emigration Using Binomial Mixture Models

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0117216 ◽  
Author(s):  
Katherine M. O’Donnell ◽  
Frank R. Thompson ◽  
Raymond D. Semlitsch
Keyword(s):  
Mixture Models ◽  
Temporary Emigration ◽  
Binomial Mixture

scholarly journals A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems

2018 ◽  
Vol 12 (2) ◽  
Author(s):  
Amir T. Payandeh Najafabadi ◽  
Saeed MohammadPour
Keyword(s):  
Regression Model ◽  
Mixture Models ◽  
Mixture Model ◽  
Negative Binomial ◽  
Mixture Distribution ◽  
Third Party ◽  
Numerical Illustration ◽  
Mixture Regression ◽  
Pure Premium ◽  
Binomial Mixture

Abstract This article introduces a k-Inflated Negative Binomial mixture distribution/regression model as a more flexible alternative to zero-inflated Poisson distribution/regression model. An EM algorithm has been employed to estimate the model’s parameters. Then, such new model along with a Pareto mixture model have employed to design an optimal rate–making system. Namely, this article employs number/size of reported claims of Iranian third party insurance dataset. Then, it employs the k-Inflated Negative Binomial mixture distribution/regression model as well as other well developed counting models along with a Pareto mixture model to model frequency/severity of reported claims in Iranian third party insurance dataset. Such numerical illustration shows that: (1) the k-Inflated Negative Binomial mixture models provide more fair rate/pure premiums for policyholders under a rate–making system; and (2) in the situation that number of reported claims uniformly distributed in past experience of a policyholder (for instance $k_1=1$ and $k_2=1$ instead of $k_1=0$ and $k_2=2$). The rate/pure premium under the k-Inflated Negative Binomial mixture models are more appealing and acceptable.

scholarly journals Microbial comparative pan-genomics using binomial mixture models

BMC Genomics ◽  
2009 ◽  
Vol 10 (1) ◽  
pp. 385 ◽  
Author(s):  
Lars Snipen ◽  
Trygve Almøy ◽  
David W Ussery
Keyword(s):  
Mixture Models ◽  
Binomial Mixture

Estimating prey abundance and distribution from camera trap data using binomial mixture models

2019 ◽  
Vol 65 (5) ◽  
Author(s):  
Hemanta Kafley ◽  
Babu R. Lamichhane ◽  
Rupak Maharjan ◽  
Bishnu Thapaliya ◽  
Nishan Bhattarai ◽  
...  
Keyword(s):  
Mixture Models ◽  
Camera Trap ◽  
Prey Abundance ◽  
Binomial Mixture ◽  
Abundance And Distribution

scholarly journals On Some Mixture Models for Over-dispersed Binary Data

2017 ◽  
Vol 6 (2) ◽  
pp. 134
Author(s):  
Bayo H. Lawal
Keyword(s):  
Mixture Models ◽  
Binary Data ◽  
Mixed Model ◽  
Gaussian Quadrature ◽  
Optimization Methods ◽  
Data Sets ◽  
Binomial Mixture ◽  
Adaptive Gaussian Quadrature ◽  
Quasi Newton ◽  
Proc Nlmixed

In this paper, we consider several binomial mixture models for fitting over-dispersed binary data. The models range from the binomial itself, to the beta-binomial (BB), the Kumaraswamy distributions I and II (KPI \& KPII) as well as the McDonald generalized beta-binomial mixed model (McGBB). The models are applied to five data sets that have received attention in various literature. Because of convergence issues, several optimization methods ranging from the Newton-Raphson to the quasi-Newton optimization algorithms were employed with SAS PROC NLMIXED using the Adaptive Gaussian Quadrature as the integral approximation method within PROC NLMIXED. Our results differ from those presented in Li, Huang and Zhao (2011) for the example data sets in that paper but agree with those presented in Manoj, Wijekoon and Yapa (2013). We also applied these models to the case where we have a $k$ vector of covariates $(x_1, x_2, \ldots, x_k)^{'}$. Our results here suggest that the McGBB performs better than the other models in the GLM framework. All computations in this paper employed PROC NLMIXED in SAS. We present in the appendix a sample of the SAS program employed for implementing the McGBB model for one of the examples.

Predicting tree recruitment with negative binomial mixture models

2012 ◽  
Vol 270 ◽  
pp. 209-215 ◽  
Author(s):  
Xiongqing Zhang ◽  
Yuancai Lei ◽  
Daoxiong Cai ◽  
Fengqiang Liu
Keyword(s):  
Mixture Models ◽  
Negative Binomial ◽  
Tree Recruitment ◽  
Binomial Mixture
Sign in / Sign up

Export Citation Format

Share Document