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Control for Omitted Variables in the Analysis of Panel and Other Longitudinal Data

Richard B. Davies, Robert Crouchley
2010 Geographical Analysis  
Davies and Robert Crouchley / 7 With exogenous variables, the model of equation (2) together with a beta mixing distribution for the outcome probability gives the stationary, zero-order, beta-logistic  ...  confidence in any inference from longitudinal data unless the statistical methodology allows for variation due to omitted variables.  ... 
doi:10.1111/j.1538-4632.1985.tb00823.x fatcat:2jmy2soybzezjmer6qc4y6e67i

A stochastic comparison study for the smallest and largest ordered statistic from Weibull-G and Gompertz Makeham distribution [article]

Madhurima Datta, Nitin Gupta
2020 arXiv   pre-print
In this paper, we have discussed the stochastic comparison of the smallest and largest ordered statistic from independent heterogeneous Weibull-G random variables and Gompertz Makeham random variables.  ...  We compare systems arising from taking different model parameters and obtain stochastic ordering results under the condition of multivariate chain majorization.  ...  In this paper we have discussed the stochastic comparison of the smallest and largest ordered statistic from independent heterogeneous Weibull-G random variables.  ... 
arXiv:2002.12474v1 fatcat:w46kabraincn7limdiawivrqje

Dispersal and ecological traits explain differences in beta diversity patterns of European beetles

Carola Gómez-Rodríguez, Andrea Freijeiro, Andrés Baselga
2015 Journal of Biogeography  
Additionally, beta diversity patterns were characterized as a multivariate pairwise dissimilarity matrix among pairs of countries for each beetle clade.  ...  Aim Disentangling the contributions of niche and dispersal processes as species distribution drivers is crucial from both theoretical and practical standpoints.  ...  ACKNOWLEDGEMENTS We thank Paula Arribas, Kirsten Miller and three anonymous referees for comments to a previous version of this manuscript.  ... 
doi:10.1111/jbi.12523 fatcat:kzt4grmqdzaxng63htd5xgzj5m

Spatial community variability: Interactive effects of predators and isolation on stochastic community assembly [article]

Rodolfo Mei Pelinson, Mathew A. Leibold, Luis C Schiesari
2020 bioRxiv   pre-print
change the relative importance of stochastic and non-stochastic processes on community variability.  ...  more abundant, irrespective of their order of colonization.  ...  This method enables statistical tests of treatment effects on both mean observed beta-diversity and mean beta-deviation (see Vannette & Fukami 2017) .  ... 
doi:10.1101/2020.12.22.423949 fatcat:k2lfy72oevawhoon76eh34owda

Bernoulli Regression Models: Revisiting the Specification of Statistical Models with Binary Dependent Variables

Jason S. Bergtold, Aris Spanos, Eberechukwu Onukwugha
2010 Journal of Choice Modeling  
The purpose of this paper is to re-examine the underlying probabilistic foundations of conditional statistical models with binary dependent variables using the PR approach.  ...  The paper provides an explicit presentation of probabilistic model assumptions, guidance on model specification and estimation, and empirical application.  ...  If ( ) j i Y f θ ; | X X is multivariate Bernoulli made up of K explanatory variables, then the index function would include an intercept, as well as first order , second order, and so on up to order K  ... 
doi:10.1016/s1755-5345(13)70033-2 fatcat:wgzqlgnvm5f2zpxtumf7suxnxy

Habitat heterogeneity drives the geographical distribution of beta diversity: the case of New Zealand stream invertebrates

Anna Astorga, Russell Death, Fiona Death, Riku Paavola, Manas Chakraborty, Timo Muotka
2014 Ecology and Evolution  
This is one of the first studies accounting for stochastic effects while examining the ecological drivers of beta diversity.  ...  We explored through a null model if beta diversity deviates from the expectation of stochastic assembly processes and whether the magnitude of the deviation varies geographically.  ...  Acknowledgments We warmly acknowledge Jari Oksanen for advice on statistical analyses and use of the R program and Nathan Kraft for advice on null model analysis.  ... 
doi:10.1002/ece3.1124 pmid:25077020 pmcid:PMC4113293 fatcat:cbieskhmmzfaxjjjzl3i6zsf2u

Stochastic Newton Sampler: R Package sns [article]

Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani
2015 arXiv   pre-print
The R package sns implements Stochastic Newton Sampler (SNS), a Metropolis-Hastings Monte Carlo Markov Chain algorithm where the proposal density function is a multivariate Gaussian based on a local, second-order  ...  When initial point is far from density peak, running SNS in non-stochastic mode by taking the Newton step, augmented with with line search, allows the MCMC chain to converge to high-density areas faster  ...  from second-order Taylor series expansion of the log-density.  ... 
arXiv:1502.02008v1 fatcat:tphctnaz4fba7mvrjr562xbp6i

Bounds for mixtures of order statistics from exponentials and applications

Eugen Păltănea
2011 Journal of Multivariate Analysis  
Usual stochastic order Hazard rate order Mixture of distributions a b s t r a c t This paper deals with the stochastic comparison of order statistics and their mixtures.  ...  For a random sample of size n from an exponential distribution with hazard rate λ, and for 1 ≤ k ≤ n, let us denote by F (λ) k:n the distribution function of the corresponding kth order statistic.  ...  Acknowledgments I am very grateful to the three referees for the careful reading of the manuscript and for all important comments and suggestions which helped to considerably improve the presentation of  ... 
doi:10.1016/j.jmva.2011.01.006 fatcat:dcnfckbshbanbe23yss2mookwi

Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold

Mehmet Balcilar, Riza Demirer, Festus V. Bekun
2021 Mathematics  
The results have significant implications for the implementation of smart beta strategies that rely heavily on the accuracy and stability of factor betas and yields.  ...  We allow a separate threshold for each parameter; thus, the parameters may shift in an unsynchronized manner such that the model moves from one state to another when the change in the parameter exceeds  ...  are usually large.  ... 
doi:10.3390/math9080915 fatcat:7bn5eu5oazhbpiqb3foudxhfti

Estimating Heterogeneous Customer Arrivals to a Large Retail store : A Bayesian Poisson model perspective
대형할인매점의 요일별 고객 방문 수 분석 및 예측 : 베이지언 포아송 모델 응용을 중심으로

Bumsoo Kim, Joonkyum Lee
2015 Korean Management Science Review  
The common effect is composed of autoregressive evolution of the parameter, which allows for analysis on seasonal effects on all multivariate time series.  ...  Still, the task of analyzing stochastic count data remains difficult and limited when it comes to multivariate count data, due to the interdependency between multiple time series.  ...  All in all, the Bayesian multivariate analysis is beneficial for multivariate count data as it can clearly separate the common effects from individual effects, as well as taking advantages from the usual  ... 
doi:10.7737/kmsr.2015.32.2.069 fatcat:zcbe2u6xovd7tf45mgth56raim

A Note on Tractable State-Space Model for Symmetric Positive-Definite Matrices

Roberto Casarin
2014 Social Science Research Network  
This article discusses the Windle and Carvalho's (2014) state-space model for observations and latent variables in the space of positive symmetric matrices.  ...  The present discussion focuses on the model specification and on the contribution to the positive-value time series literature.  ...  in the background section, a discussion on the inferential di culties that one might encounter in multivariate stochastic volatility modeling.  ... 
doi:10.2139/ssrn.2535282 fatcat:2mxueph5krc3tgnrl6beaf4coi

A Physical Analysis of Polarimetric SAR Data Statistical Models

Xinping Deng, Carlos Lopez-Martinez, Eduardo Makhoul Varona
2016 IEEE Transactions on Geoscience and Remote Sensing  
Statistical analysis of the simulated data shows that the distribution of the scatterer response has an effect only when the number of scatterers in a resolution cell is very small, which appears in very  ...  The mixture of point targets and distributed targets will lead to an extremely heterogeneous appearance, which may be a clue to analyze the urban areas in polarimetric SAR data.  ...  -2 data provided in the framework of the AgriSAR 2009 campaign, the Japan Aerospace Exploration Agency (JAXA) for the ALOS-2 data provided in the framework of the 4th ALOS Research Announcement for ALOS  ... 
doi:10.1109/tgrs.2015.2510399 fatcat:m4jbkazrgfgargtprjocs5jo7i

Origin of the dust bunny distribution in ecological community data

Bruce McCune, Heather T. Root
2015 Plant Ecology  
The distribution of sample units in multivariate species space typically departs strongly from the 1 multivariate normal distribution.  ...  Dust bunny 16 intensity depends not only on population processes and disturbance, but also on the properties of the 17 sample, such as sample unit area or volume. 18 19 20 21 Our goals diverge from studies  ...  We thank contributors of data; students and colleagues for helpful discussion; Dave Roberts for sharing a draft chapter on schools of community ecology, Amy Charron for dust bunny drawings, and Stéphane  ... 
doi:10.1007/s11258-014-0404-1 fatcat:4n6uldhgdjfjjgs32urcgmxjbe

Stochastic Newton Sampler: The R Package sns

Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T. A. Sharabiani
2016 Journal of Statistical Software  
The R package sns implements Stochastic Newton Sampler (SNS), a Metropolis-Hastings Monte Carlo Markov Chain algorithm where the proposal density function is a multivariate Gaussian based on a local, second-order  ...  When initial point is far from density peak, running SNS in non-stochastic mode by taking the Newton step -augmented with line search -allows the MCMC chain to converge to high-density areas faster.  ...  Discussion In this paper we presented sns, an R package for Stochastic Newton Sampling of twicedifferentiable, log-concave PDFs, where a multivariate Gaussian resulting from second-order Taylor series  ... 
doi:10.18637/jss.v074.c02 fatcat:xnmwj4z72rc7xnvf3bwy76x5ha

Integrative analysis of time course metabolic data and biomarker discovery [article]

Takoua Jendoubi, Timothy M.D. Ebbels
2018 arXiv   pre-print
of the multivariate information intrinsic to the data or iv) unable to uncover multiple associations between different omic data.  ...  by augmenting the mixed-effects model with a conditional auto-regressive (CAR) component and iv) identify potential associations between heterogeneous omic variables .  ...  The DPPCA model is a multivariate model using PCA, where PCA scores are modeled via a stochastic volatility model.  ... 
arXiv:1801.07767v2 fatcat:daea5fytobfdxcpzkrdeiprdc4
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