Analysis of factors affecting superovulatory responses in ruminants

1995 ◽  
Vol 124 (1) ◽  
pp. 61-70 ◽  
Author(s):  
J. A. Woolliams ◽  
Z. W. Luo ◽  
B. Villanueva ◽  
D. Waddington ◽  
P. J. Broadbent ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Commercial Application ◽  
Data Set ◽  
Factors Affecting ◽  
Binomial Distributions ◽  
Breeding Schemes ◽  
Over Dispersion ◽  
Mean Variance

SUMMARYData on ovulation rate and numbers of ova and transferable embryos recovered from superovulated cattle and sheep were analysed using generalized linear models, quasi-likelihood, restricted maximum likelihood (REML) and generalized linear mixed models (GLMMS). The data pertained to the operation of nucleus breeding schemes in cattle and the commercial application of embryo transfer in sheep.Results of the analyses showed that generalized linear models involving Poisson and Binomial distributions were inappropriate because of over-dispersion, and that analyses using quasi-likelihood to model negative binomial and β-binomial distributions were more suitable. Factors identified as important in determining the results in cattle were the number of previous superovulations (a higher proportion of transferable embryos were obtained in the initial flush compared to subsequent recoveries in two out of three sets of data), the donor (significant in all analyses with repeated recoveries) and its mate (significant in some analyses). In sheep, the use of pFSH or hMG for superovulation increased embryo yields above those obtained with PMSG + GnRH. Analyses of a further data set for sheep showed the effect of breed was ambiguous.The effects of donors and their mates were treated as random effects in analyses involving REML and GLMMS. Results showed that the repeatability of the number of transferable embryos produced per donor ranged between 0·13 and 0·23 in three sets of data and was significant in all cases. In these analyses the variance among mates was not significantly different from zero.The results of analyses were used to develop a random generator to simulate the numbers of ova and embryos recovered from a cow following superovulation. By sampling from negative binomial distributions where the scale factor used for each cow was a normally distributed deviate, distributions were obtained which had the same mean, variance and repeatability as those observed.

Crash Frequency Analysis with Generalized Additive Models

2008 ◽  
Vol 2061 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Yuanchang Xie ◽  
Yunlong Zhang
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Generalized Additive Model ◽  
Negative Binomial ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Crash Frequency ◽  
Data Set ◽  
Explanatory Variables

Recent crash frequency studies have been based primarily on generalized linear models, in which a linear relationship is usually assumed between the logarithm of expected crash frequency and other explanatory variables. For some explanatory variables, such a linear assumption may be invalid. It is therefore worthwhile to investigate other forms of relationships. This paper introduces generalized additive models to model crash frequency. Generalized additive models use smooth functions of each explanatory variable and are very flexible in modeling nonlinear relationships. On the basis of an intersection crash frequency data set collected in Toronto, Canada, a negative binomial generalized additive model is compared with two negative binomial generalized linear models. The comparison results show that the negative binomial generalized additive model performs best for both the Akaike information criterion and the fitting and predicting performance.

Factors affecting commercial trawl fleet landings in a multispecies fishery

2003 ◽  
Vol 83 (1) ◽  
pp. 205-215 ◽  
Author(s):  
M. Labropoulou ◽  
C.D. Maravelias ◽  
C. Papaconstantinou
Keyword(s):  
Total Variation ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Factors Affecting ◽  
Abundance Indices ◽  
Vessel Capacity ◽  
Main Effects ◽  
Trawl Fisheries ◽  
Fishing Area ◽  
Modelling Approach

Retained catches of trawl fleet in Chalkis fishing port-market, Greece, were analysed using generalized linear models (GLMs) in an attempt to identify factors that influence the total landings of the groundfish fisheries. Main effects in the model included a factor for vessel tonnage that determined fishing power, as well as factors for month and fishing area. Covariates examined were all found to have a significant effect on the retained catches, with month and vessel capacity alone explaining 38% and 30% respectively of the total variation of the data. Significant interactions observed indicate that the fluctuations in retained catch differed by fishing area and month as well as by fishing area and vessel category. Within vessel categories, month and fishing area also had significant effects on the retained catches, with fishing area being more important than month for the smallest vessels. Results indicate that the modelling approach of retained catches from trawl fisheries is a promising method for obtaining representative abundance indices.

Modeling of the spatial distribution of Pacific spiny dogfish (Squalus suckleyi) in the Gulf of Alaska using generalized additive and generalized linear models

2013 ◽  
Vol 70 (9) ◽  
pp. 1372-1385 ◽  
Author(s):  
Jason R. Gasper ◽  
Gordon H. Kruse
Keyword(s):  
Spatial Distribution ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Fish Habitat ◽  
Additive Model ◽  
Gulf Of Alaska ◽  
Spiny Dogfish ◽  
Essential Fish Habitat ◽  
Mortality Estimation

The Pacific spiny dogfish (Squalus suckleyi) is a common bycatch species in the Gulf of Alaska. Their spatial distribution is poorly understood, as most catch is discarded at sea. We analyzed spiny dogfish spatial distribution from fishery-dependent and -independent observations of longline gear between 1996 and 2008 using generalized additive and generalized linear models. Poisson, negative binomial, and quasi-Poisson error structures were investigated; the quasi-Poisson generalized additive model fit best. Models showed that spiny dogfish catches were concentrated east of Kodiak Island in waters ≤100 m deep. Results facilitate design of future spiny dogfish assessment surveys and identification of areas in which to focus at-sea observations for fishing mortality estimation, and provide the basis for first-ever designation of spiny dogfish essential fish habitat, despite US legal requirements for essential fish habitat designations since 1996. Identified areas of high bycatch may expedite spatial management by indicating areas in which directed spiny dogfish fisheries could be focused or, conversely, areas in which heightened conservation and catch accounting efforts would be most effective to prevent overfishing of this long-lived, late-maturing species.

Models of the Fish Yield from Lakes: Does the Random Component Matter?

1991 ◽  
Vol 48 (4) ◽  
pp. 619-622 ◽  
Author(s):  
Chris D. Bajdik ◽  
David C. Schneider
Keyword(s):  
Generalized Linear Models ◽  
Random Error ◽  
Linear Models ◽  
Parameter Estimates ◽  
Random Component ◽  
Lake Area ◽  
Data Set ◽  
Fish Yield ◽  
Pearson Residuals ◽  
Distributional Assumptions

Generalized linear models were used to investigate the sensitivity of paramater estimates to choice of the random error assumption in models of fisheries data. We examined models of fish yield from lakes as a function of (i) Ryder's morphoedaphic index, (ii) lake area, lake depth, and concentration of dissolved solids, and (iii) fishing effort. Models were fit using a normal, log-normal, gamma, or Poisson distribution to generate the random error. Plots of standardized Pearson residuals and standardized deviance residuals were used to evaluate the distributional assumptions. For each data set, observations were found to be consistent with several distributions; however, some distributions were shown to be clearly inappropriate. Inappropriate distributional assumptions produced substantially different parameter estimates. Generalized linear models allow a variety of distributional assumptions to be incorporated in a model, and thereby let us study their effects.

scholarly journals Chilling to the bone: Lower temperatures increase vertebrate predation by Tonatia bidens (Chiroptera: Phyllostomidae)

2020 ◽  
Vol 37 ◽  
pp. 1-5
Author(s):  
Fernando Carvalho ◽  
Daniela A.S. Bôlla ◽  
Viviane Mottin ◽  
Suelen Zonta Kiem ◽  
Jairo J. Zocche ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Generalized Linear Models ◽  
Negative Binomial Distribution ◽  
Diet Composition ◽  
Linear Models ◽  
Negative Binomial ◽  
Meteorological Station ◽  
Foraging Patterns ◽  
Influence Of Temperature ◽  
The North

The greater round-eared bat, Tonatia bidens (Spix, 1823), is a medium-sized phyllostomid bat distributed in the north of Argentina, Paraguay and Brazil. The diet and foraging patterns of this species are poorly known. We analyzed the composition of the diet of a population of T. bidens and how the temperature influences the consumption of vertebrates and invertebrates. To describe diet composition, we conducted weekly collections of food scrap from two monospecific night-perches. Data of temperature for the study period were taken from the meteorological station installed 300 m from the collection perches. The influence of temperature was evaluated using generalized linear models (GLMs) with negative binomial distribution. Tonatia bidens consumed 28 taxons (204 records), being at least 17 Artropods and 11 Passeriformes birds. Temperature explained a greater proportion of vertebrate abundance (R2 = 0.23) than invertebrate (R2 = 0.16) or to both pooled (R2 = 0.11). The relation with temperature was positive with invertebrates and negative with the vertebrates. The diet of the population of T. bidens comprised mainly invertebrates, which were the most frequent and diverse taxa. Data suggests that T. bidens has a diverse diet, with proportion of the item’s consumption varying temporally. Environmental factors, such as the temperature presented on this work, seems to be good proxies for the dietary traits of this species.

scholarly journals Weighted Negative Binomial Poisson-Lindley Distribution with Actuarial Applications

2018 ◽  
Author(s):  
Hossein Zamani ◽  
Noriszura Ismail ◽  
Marzieh Shekari
Keyword(s):  
Maximum Likelihood Method ◽  
Negative Binomial ◽  
Statistical Tests ◽  
Discrete Distribution ◽  
Likelihood Method ◽  
Weighted Version ◽  
Lindley Distribution ◽  
Weighted Distribution ◽  
Binomial Distributions ◽  
Over Dispersion

This study introduces a new discrete distribution which is a weighted version of Poisson-Lindley distribution. The weighted distribution is obtained using the negative binomial weight function and can be fitted to count data with over-dispersion. The p.m.f., p.g.f. and simulation procedure of the new weighted distribution, namely weighted negative binomial Poisson-Lindley (WNBPL), are provided. The maximum likelihood method for parameter estimation is also presented. The WNBPL distribution is fitted to several insurance datasets, and is compared to the Poisson and negative binomial distributions in terms of several statistical tests.

scholarly journals Eucalyptus cloeziana seed count data: a comparative analysis of statistical models

2019 ◽  
Vol 43 ◽  
Author(s):  
Thomas Bruno Michelon ◽  
Cesar Augusto Taconeli ◽  
Elisa Serra Negra Vieira ◽  
Maristela Panobianco
Keyword(s):  
Count Data ◽  
Statistical Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Germination Test ◽  
Seed Technology ◽  
Tukey Test ◽  
Binomial Distributions ◽  
Eucalyptus Cloeziana ◽  
Genus Eucalyptus

ABSTRACT Generalized linear models (GLMs) are an extension of the linear model and include the normal, Poisson, and negative binomial distributions. Although GLMs were introduced in 1972, most seed technology studies, especially those involving count data, such as germination tests of seeds from the genus Eucalyptus, still using the analysis of variance, without analysis of the fit of other models. Thus, this study aimed to evaluate the most appropriate model in the GLM class for seed count data of Eucalyptus cloeziana. Data were obtained from a germination test using seeds from three lots of E. cloeziana. Each lot was separated by sieving into three material fractions based on size: small (<0.84 mm), medium (from 1.18 to 1.00 mm), and large (>1.18 mm). The data analysis was based on the use of GLMs adjusted to normal, Poisson, and negative binomial distributions, and the models were evaluated by the Akaike and Bayesian Schwartz criteria and Cook’s distance and half-normal diagnostic graphs. Compared to other adjustments, the normal distribution adjustment differed in the configuration of means submitted to the Tukey test, and although the data met all normality assumptions, the adjustment with the Poisson distribution was the most suitable for the count data from a germination test of E. cloeziana seeds.

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