Academic literature on the topic 'Binomial GLM'

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Journal articles on the topic "Binomial GLM"

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Santi, Vera Maya, Abi Wiyono, and Sudarwanto. "Pemodelan Jumlah Kasus Malaria di Indonesia Menggunakan Generalized Linear Model." Jurnal Statistika dan Aplikasinya 5, no. 1 (2021): 112–20. http://dx.doi.org/10.21009/jsa.05111.

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Generalized Linear Model (GLM) telah banyak digunakan untuk memodelkan berbagai macam tipe data dimana distribusi dari variabel respon merupakan distribusi yang termasuk dalam distribusi keluarga eksponensial. Contoh umum dari distribusi keluarga eksponensial adalah distribusi Poisson dan Binomial. Model regresi GLM mendeskripsikan struktur dari variabel prediktor, sedangkan fungsi penghubung secara khusus mendeskripsikan hubungan antara model regresi dengan nilai ekspektasi dari variabel respon. Tujuan dari artikel ini adalah mendapatkan variabel-variabel prediktor yang berpengaruh signifikan
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Lawal, Bayo H. "On Some Mixture Models for Over-dispersed Binary Data." International Journal of Statistics and Probability 6, no. 2 (2017): 134. http://dx.doi.org/10.5539/ijsp.v6n2p134.

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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
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Zorn, Christopher. "A Solution to Separation in Binary Response Models." Political Analysis 13, no. 2 (2005): 157–70. http://dx.doi.org/10.1093/pan/mpi009.

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A common problem in models for dichotomous dependent variables is “separation,” which occurs when one or more of a model's covariates perfectly predict some binary outcome. Separation raises a particularly difficult set of issues, often forcing researchers to choose between omitting clearly important covariates and undertaking post—hoc data or estimation corrections. In this article I present a method for solving the separation problem, based on a penalized likelihood correction to the standard binomial GLM score function. I then apply this method to data from an important study on the postwar
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Hay, Fiona R., Andrew Mead, and Mark Bloomberg. "Modelling seed germination in response to continuous variables: use and limitations of probit analysis and alternative approaches." Seed Science Research 24, no. 3 (2014): 165–86. http://dx.doi.org/10.1017/s096025851400021x.

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AbstractProbit-based models relating a proportional response variable to a temporal explanatory variable, assuming that the times to response are normally distributed within the population, have been used in seed biology for describing the rate of loss of viability during seed ageing and the progress of germination over time in response to environmental signals (e.g. water, temperature). These models may be expressed as generalized linear models (GLMs) with a probit (cumulative normal distribution) link function, and, using GLM fitting procedures in current statistical software, parameters of
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Lawal, Bayo H. "GLM for Some Class of Com-Poisson Distributions with Applications." International Journal of Statistics and Probability 7, no. 6 (2018): 1. http://dx.doi.org/10.5539/ijsp.v7n6p1.

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In this paper, we present regression models (GLM) for the class of Conway-Maxwell-Poisson (Com-Poisson) distributions. This class of models include the Com-Poisson, the Com-Poisson negative binomial, the Generalized Com-Poisson and the Extended Com-Poisson distributions, all of which have been presented in various literatures within the last five years. While these distributions have been applied most especially to frequency count data exhibiting over or under dispersion, not much has been presented in the application of this class of models to data having several covariates (the exception bei
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Bosquet, Clément, and Hervé Boulhol. "Applying the GLM Variance Assumption to Overcome the Scale-Dependence of the Negative Binomial QGPML Estimator." Econometric Reviews 33, no. 7 (2014): 772–84. http://dx.doi.org/10.1080/07474938.2013.806102.

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Qazi, Sanjive, David Nam, Larn Hwang, Vuong Trieu, and Fatih Uckun. "ATIM-10. CLINICAL PREDICTORS OF RESPONSE FOR RECURRENT/REFRACTORY GLIOBLASTOMA MULTIFORME (GBM) AND ANAPLASTIC ASTROCYTOMA (AA, WHO GRADE III) PATIENTS TREATED WITH THE ANTI-TGFß2 RNA THERAPEUTIC OT-101." Neuro-Oncology 21, Supplement_6 (2019): vi3. http://dx.doi.org/10.1093/neuonc/noz175.010.

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Abstract BACKGROUND OT-101 is a first-in-class αTGF-ß2 RNA therapeutic and exhibits significant single-agent activity in patients with recurrent/refractory (R/R) GBM and AA. Here we report significant predictors of best overall response (BOR = CR, PR, stable disease >6 months) and improved OS for R/R GBM and AA patients after treatment with OT-101 on the Phase II study NCT00431561. METHODS A generalized linear model (GLM; Binomial distribution, logit function link function) was fitted to explore several clinical parameters as potential predictors for BOR. The parametric survival model fitte
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Lee, Simon CK. "Delta Boosting Implementation of Negative Binomial Regression in Actuarial Pricing." Risks 8, no. 1 (2020): 19. http://dx.doi.org/10.3390/risks8010019.

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This study proposes an efficacious approach to analyze the over-dispersed insurance frequency data as it is imperative for the insurers to have decisive informative insights for precisely underwriting and pricing insurance products, retaining existing customer base and gaining an edge in the highly competitive retail insurance market. The delta boosting implementation of the negative binomial regression, both by one-parameter estimation and a novel two-parameter estimation, was tested on the empirical data. Accurate parameter estimation of the negative binomial regression is complicated with c
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Camarero, Mariam, Laura Montolio, and Cecilio Tamarit. "Understanding German FDI in Latin America and Asia: A Comparison of GLM Estimators." Economies 8, no. 1 (2020): 19. http://dx.doi.org/10.3390/economies8010019.

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The growth of Foreign Direct Investment (FDI) in developing countries over the last decade has attracted an intense academic and policy-oriented interest for its determinants. Despite the gravity model being considered a useful tool to approximate bilateral FDI flows, the literature has seen a growing debate in relation to its econometric specification, so that which is the best estimator for the gravity equation is far from conclusive. This paper examines the determinants of German outward FDI in Latin America and Asia for the period 1996-2012 by evaluating the performance of alternative Gene
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Faris, Richard, and Neil Paton. "121 Statistical Analysis Method Counts for Sow Count Data Responses." Journal of Animal Science 99, Supplement_1 (2021): 56. http://dx.doi.org/10.1093/jas/skab054.094.

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Abstract Several statistical analysis methods are typically employed to analyze sow reproductive count data. The research objective was to compare analysis methods of pig birth counts to determine their robustness in identifying simulated treatment differences. Counts of stillborn (SB), born alive (BA) and sow parity differences were simulated using descriptive statistics from a sow farm. Different scenarios were tested: 1) Effect of a 0.5, 1.0, 1.5, and 2.0 percentage point change in treatment difference in SB and BA and, 2) Replicates of 20 to 200 experimental units (EU) in increments of 20
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Dissertations / Theses on the topic "Binomial GLM"

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Brodersson, Anna Lilly. "Flygbesiktning av Luftledningar : Modellering av samband mellan besiktningsanmärkningar och systemtillförlitlighet." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219572.

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This paper thoroughly investigates needs and requirements for overhead distribution feeder inspection and develops models to investigate possible relations between short term inspections remarks and outages. The study was conducted in collaboration with Fortum Distribution AB that supplied extensive information about their overhead power feeders concerning both inspection and power outages. The investigated models where lognormal linear model, Poisson generalized linear model and negative binomial generalized linear model. All models were implemented utilizing offset terms to compensate for di
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Zeileis, Achim, Christian Kleiber, and Simon Jackman. "Regression Models for Count Data in R." Foundation for Open Access Statistics, 2008. http://epub.wu.ac.at/4986/1/Zeileis_etal_2008_JSS_Regression%2DModels%2Dfor%2DCount%2DData%2Din%2DR.pdf.

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The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle
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Zeileis, Achim, Christian Kleiber, and Simon Jackman. "Regression Models for Count Data in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/1168/1/document.pdf.

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The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of zero-inflated and hurdle regression models in the functions zeroinfl() and hurdle() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both model c
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Amoêdo, Pedro Marinho. "Modelos para dados de proporção: um estudo sobre a viabilidade de ovos do mosquito Aedes aegypti, mantidos em diferentes tipos de armazenamentos na Amazônia." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-28072014-105923/.

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O presente trabalho teve por objetivo estimar o tempo que os ovos de Aedes aegypti permanecem em condições de eclodir e produzir larvas viáveis após permanecerem armazenados por determinado tempo ( viabilidade) em diferentes tipos de recipientes. Para tal, usou-se a metodologia dos modelos lineares generalizados para dados na forma de proporção. Como função de ligação, optou-se pelas funções logística e probito, como preditores lineares retas paralelas e retas concorrentes. Foi realizado um experimento, em que procurou-se simular, tanto quanto possível, o efeito das condições ambientais que os
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Gu, Chenchen. "Option Pricing Using MATLAB." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/382.

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This paper describes methods for pricing European and American options. Monte Carlo simulation and control variates methods are employed to price call options. The binomial model is employed to price American put options. Using daily stock data I am able to compare the model price and market price and speculate as to the cause of difference. Lastly, I build a portfolio in an Interactive Brokers paper trading [1] account using the prices I calculate. This project was done a part of the masters capstone course Math 573: Computational Methods of Financial Mathematics.
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Book chapters on the topic "Binomial GLM"

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Williams, R. "Binomial model." In Graduate Studies in Mathematics. American Mathematical Society, 2006. http://dx.doi.org/10.1090/gsm/072/02.

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Jantzen, Jens. "Gaussian binomial coefficients." In Lectures on Quantum Groups. American Mathematical Society, 1995. http://dx.doi.org/10.1090/gsm/006/02.

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Dunn, Peter K., and Gordon K. Smyth. "Chapter 9: Models for Proportions: Binomial GLMs." In Springer Texts in Statistics. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4419-0118-7_9.

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Zuur, A. F., A. Mira, F. Carvalho, et al. "Negative Binomial GAM and GAMM to Analyse Amphibian Roadkills." In Statistics for Biology and Health. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87458-6_16.

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Dunn, Peter K., and Gordon K. Smyth. "Chapter 10: Models for Counts: Poisson and Negative Binomial GLMs." In Springer Texts in Statistics. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4419-0118-7_10.

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Hector, Andy. "Binomial GLMs." In The New Statistics with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198798170.003.0017.

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GLMs with a binomial distribution are designed for the analysis of binomial counts (how many times something occurred relative to the total number of possible times it could have occurred). A logistic link function constrains predictions to be above zero and below the maximum using the S-shaped logistic curve. Overdispersion can be diagnosed and dealt with using a quasi-maximum likelihood extension to GLM analysis.
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Touchon, Justin C. "Generalized Linear Models (GLM)." In Applied Statistics with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198869979.003.0007.

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Chapter 7 introduces one of the most useful statistical frameworks for the modern life scientist: the generalized linear model (GLM). GLMs extend the linear model to an array of non-normally distributed data such as Poisson, negative binomial, binomial, and Gamma distributed data. These models dramatically improve the breadth of data that can be properly analysed without resorting to non-parametric statistics. Using the same RxP dataset, readers learn how to assess the error distribution of their data and evaluate competing models to achieve the best, most robust analysis possible. Diagnostic plots and assessing model fit is continually taught as is how to interpret the model output and calculate summary statistics. Plotting non-normal error distributions with ggplot2 is taught, as is using the predict() function.
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Kéry, Marc. "Binomial Mixed-Effects Model (Binomial GLMM)." In Introduction to WinBUGS for Ecologists. Elsevier, 2010. http://dx.doi.org/10.1016/b978-0-12-378605-0.00019-3.

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Hector, Andy. "GLMs for Binary Data." In The New Statistics with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198798170.003.0018.

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Hidalgo, Luis F., Josep Rialp, and David Urbano. "Are There Really Differences Between Social and Commercial Entrepreneurship in Developing Countries?" In Handbook of Research on Smart Territories and Entrepreneurial Ecosystems for Social Innovation and Sustainable Growth. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2097-0.ch017.

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The objective of this chapter is to determine the probability of starting social or commercial entrepreneurship in developing countries using the institutional approach as the theoretical framework. The study tests the hypotheses through a binomial logistic regression based on a sample of 10,598 entrepreneurs obtained from the Global Entrepreneurship Monitor (GEM). The main findings demonstrate that a higher level of education (formal institution) and a positive perception of personal values (informal institution) increase the probability of being a social entrepreneur. Also, the study shows that the interaction between informal institutions causes changes in the likelihood of being a social or commercial entrepreneur. This research advances the discipline by providing new information on the institutional environmental factors that influence social entrepreneurial activity.
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