Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood

Biometrics ◽  
1997 ◽  
Vol 53 (3) ◽  
pp. 983 ◽  
Author(s):  
Michael G. Kenward ◽  
James H. Roger
Keyword(s):  
Maximum Likelihood ◽  
Fixed Effects ◽  
Restricted Maximum Likelihood ◽  
Small Sample

GENETIC PARAMETERS FOR CALVING EASE IN AYRSHIRES

1990 ◽  
Vol 70 (1) ◽  
pp. 67-71 ◽  
Author(s):  
R. I. CUE
Keyword(s):  
Maximum Likelihood ◽  
Key Words ◽  
Genetic Correlation ◽  
Direct Effect ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Random Effect ◽  
Restricted Maximum Likelihood ◽  
Calving Ease ◽  
Likelihood Model

Estimates of genetic parameters of calving ease were obtained in Ayrshires. A restricted maximum likelihood model was used with the fixed effects of herd, month-season of calving, sex of calf and dam weight, and the random effect of sire (of calf). The heritability of the direct effect in heifers and in adult cows was approximately 2%, with a genetic correlation between the direct effect in heifers and in adult cows of close to 70%. Key words: Variance, heritability, calving ease, Ayrshire

scholarly journals Genetic parameters for stayability in Murrah buffaloes

2010 ◽  
Vol 77 (2) ◽  
pp. 252-256 ◽  
Author(s):  
Priscilla M Galeazzi ◽  
Maria EZ Mercadante ◽  
Josineudson AIIV Silva ◽  
Rúsbel R Aspilcueta-Borquis ◽  
Gregório MF de Camargo ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Fixed Effects ◽  
Threshold Model ◽  
Genetic Effect ◽  
Restricted Maximum Likelihood ◽  
Additive Genetic Effect ◽  
Additive Effects ◽  
Birth Season ◽  
Breeding Programmes ◽  
Breeding Herd

In order to contribute to the breeding programmes of Asian water buffalo, the aim of this study was to analyse the influence of genetic effects in the stayability of Murrah dairy buffaloes. The stayability trait (ST) was defined as the female's ability to stay in the herd for one (ST1), two (ST2), three (ST3), four (ST4), five (ST5) or six years (ST6) after the first calving. The same trait was also considered as continuous and was designated stayability in days up to one (STD1), two (STD2), three (STD3), four (STD4), five (STD5) or six years (STD6) after the first calving. Data from 1016 females reared in nine herds located in the State of São Paulo, Brazil, were analysed. Statistical models included the additive genetic effect of the animal and the fixed effects of the buffalo breeding herd, birth year and birth season. Additive effects for ST were estimated by approximate restricted maximum likelihood using a threshold model, while for STD, the additive effects were estimated by restricted maximum likelihood. Heritability estimates were lower for ST, except for ST1, (0·11±0·07, 0·17±0·06, 0·23±0·06, 0·16±0·08, 0·14±0·09 and 0·16±0·10 for ST1, ST2, ST3, ST4, ST5 and ST6, respectively) when compared with STD (0·05±0·06, 0·18±0·08, 0·40±0·10, 0·49±0·11, 0·41±0·11 and 0·30±0·13, for STD1, STD2, STD3, STD4, STD5 and STD6, respectively). Considering the values of heritability and owing to the serial nature of STD to a specific age, selection for STD3 should have a favourable influence on STD to other ages.

Fixed effects in rare events data: a penalized maximum likelihood solution

2018 ◽  
Vol 8 (1) ◽  
pp. 92-105 ◽  
Author(s):  
Scott J. Cook ◽  
Jude C. Hays ◽  
Robert J. Franzese
Keyword(s):  
Maximum Likelihood ◽  
Fixed Effects ◽  
Rare Events ◽  
Small Sample ◽  
Baseline Risk ◽  
Main Concern ◽  
Cross Sectional ◽  
Penalized Maximum Likelihood ◽  
Fixed Effects Models ◽  
Events Data

AbstractMost agree that models of binary time-series-cross-sectional data in political science often possess unobserved unit-level heterogeneity. Despite this, there is no clear consensus on how best to account for these potential unit effects, with many of the issues confronted seemingly misunderstood. For example, one oft-discussed concern with rare events data is the elimination of no-event units from the sample when estimating fixed effects models. Many argue that this is a reason to eschew fixed effects in favor of pooled or random effects models. We revisit this issue and clarify that the main concern with fixed effects models of rare events data is not inaccurate or inefficient coefficient estimation, but instead biased marginal effects. In short, only evaluating event-experiencing units gives an inaccurate estimate of the baseline risk, yielding inaccurate (often inflated) estimates of predictor effects. As a solution, we propose a penalized maximum likelihood fixed effects (PML-FE) estimator, which retains the complete sample by providing finite estimates of the fixed effects for each unit. We explore the small sample performance of PML-FE versus common alternatives via Monte Carlo simulations, evaluating the accuracy of both parameter and effects estimates. Finally, we illustrate our method with a model of civil war onset.

Characteristics and heritability analysis of head scales of the Hungarian meadow viper (Vipera ursinii rakosiensis, Méhely 1893)

2012 ◽  
Vol 33 (3-4) ◽  
pp. 393-400 ◽  
Author(s):  
Bálint Üveges ◽  
Bálint Halpern ◽  
Tamás Péchy ◽  
János Posta ◽  
István Komlósi
Keyword(s):  
Animal Model ◽  
Maximum Likelihood ◽  
Random Effects ◽  
Fixed Effects ◽  
Maximum Likelihood Method ◽  
Restricted Maximum Likelihood ◽  
Likelihood Method ◽  
Covariance Components ◽  
Heritability Analysis ◽  
Variance And Covariance Components

The objective of our research was to determine the heritability of head scale numbers of Vipera ursinii rakosiensis. 430 specimens (177 males and 253 females) were included in the analysis, most of which were born and raised in the Hungarian Meadow Viper Conservation Centre between 2004 and 2008. Due to the controlled breeding conditions, the dams of the offspring were known, and the sires were known in 51% of the cases. Only the ancestors of the wild caught specimens were unknown, but these animals were included as parents in the analysis. Photographic identification was used to identify and characterise the specimens, the majority over consecutive years. We counted the following scales: loreal-, circumocular-, apical-, and crown (intercanthal- and intersupraocular-) shields, as well as presence-absence data of other characteristics which are detailed further in the article. The variance and covariance components were determined via the restricted maximum likelihood method. The repeatability animal model consisted of the year of birth and the sex of the snakes as fixed effects, the dam as permanent environmental, and the animal as random effects. Heritability values varied between 0.32 and 0.70. We also report scale numbers and statistics of differences between scale numbers of sexes.

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