S-Estimation and a Robust Conditional Akaike Information Criterion for Linear Mixed Models

The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (−2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes.

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SELECTION OF Coffea arabica L. HYBRIDS USING MIXED MODELS WITH DIFFERENT STRUCTURES OF VARIANCE-COVARIANCE MATRICES

Coffee Science ◽

10.25186/cs.v13i3.1444 ◽

2018 ◽

Vol 13 (3) ◽

pp. 304

Author(s):

Fernanda Aparecida Castro Pereira ◽

Samuel Pereira de Carvalho ◽

Tiago Teruel Rezende ◽

Leonardo Luiz Oliveira ◽

Diego Rosa Baquião Maia

Keyword(s):

Akaike Information Criterion ◽

Mixed Models ◽

Coffea Arabica ◽

Moving Average ◽

Information Criterion ◽

Mixed Linear Model ◽

Covariance Matrices ◽

Components Of Variance ◽

R Matrix ◽

Estimate Variance

This study aimed to evaluate different structures of variance-covariance matrices in modeling of productive performance of coffee genotypes over the years, and select hybrids of <em>Coffea arabica</em> using mixed models. A mixed linear model was used to estimate variance components, heritability coefficients, and prediction of genetic values of hybrids and cultivars. Three commercial cultivars and eight hybrids of <em>C. arabica</em> L. were evaluated. The field production after acclimatization of seedlings was conducted in March 2006. The yield averages from 2009, 2010, 2011, 2013, and 2014 agricultural years were evaluated. The selection criteria of models were used to test 10 structures of variance-covariance matrices, and later a model was chosen to estimate the components of variance, heritability coefficients, and prediction of genetic values. According to Bayesian information criterion (BIC), the best structure was ARMA (Autoregressive Moving Average); however, considering the Akaike Information Criterion (AIC) and corrected Akaike Information Criterion (AICC), the CSH (Heterogeneous Composite Symmetric) was indicated. The Spearman correlation between the genotypic values obtained in the models with ARMA and CSH type R matrix was 0.84. The high and positive correlation indicates that the best model could involve the R matrix with ARMA or CSH structure. The heritability of individual genotypes differed from heritability in broad sense, which considers the independence among agricultural years. Hybrids with higher performance were identified by ordering the genotypic effects, among them, H 2.2, H 4.2, and H 6.1 hybrids were highlighted.

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An assumption for the development of bootstrap variants of the Akaike information criterion in mixed models

Statistics & Probability Letters ◽

10.1016/j.spl.2007.12.015 ◽

2008 ◽

Vol 78 (12) ◽

pp. 1422-1429 ◽

Cited By ~ 5

Author(s):

Junfeng Shang ◽

Joseph E. Cavanaugh

Keyword(s):

Akaike Information Criterion ◽

Mixed Models ◽

Information Criterion

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Relationship between chorusing activity and number of oviposition events in the Otton frog

Animal Biology ◽

10.1163/15707563-17000143 ◽

2018 ◽

Vol 68 (1) ◽

pp. 105-111 ◽

Cited By ~ 3

Author(s):

Noriko Iwai

Keyword(s):

Akaike Information Criterion ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Time Lag ◽

Information Criterion ◽

Effective Technique ◽

Breeding Activity ◽

Minute Period ◽

A Site ◽

The Relationship

Abstract Call surveys are an effective technique for detecting the presence and activity of breeding male frogs. Such surveys have been used to quantify breeding activity at a site under the assumption that male chorusing activity appropriately reflects breeding consequences, such as the number of oviposition events. However, only a few studies have actually examined the relationship between chorusing activity and breeding consequences in the field. In this study, I examined the relationship between chorusing activity (the number of male calls recorded during a five-minute period every night) and the number of oviposition events (number of oviposited egg masses during the night) of the Otton frog, Babina subaspera, with regard to the time lag between calls and oviposition. I constructed nine generalized linear mixed models (GLMMs) to explain the number of oviposition events by chorusing activity on the same night and on nights 1 to 7 days before the oviposition events. The Akaike information criterion (AiC) of the GLMM was lowest when the number of calls from nights 2 days before the oviposition events was used, indicating that breeding consequences in Otton frogs reflect the chorusing activity of 2 days prior. This study shows that frog call surveys can be reliable tools with which to represent breeding activity at a site as long as the time lag between chorusing activity and breeding consequences is considered.

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An Empirical Bayes Information Criterion for Selecting Variables in Linear Mixed Models

JOURNAL OF THE JAPAN STATISTICAL SOCIETY ◽

10.14490/jjss.40.111 ◽

2010 ◽

Vol 40 (1) ◽

pp. 111-131 ◽

Cited By ~ 2

Author(s):

Tatsuya Kubokawa ◽

Muni S. Srivastava

Keyword(s):

Mixed Models ◽

Empirical Bayes ◽

Linear Mixed Models ◽

Information Criterion ◽

Bayes Information Criterion

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On the behaviour of marginal and conditional AIC in linear mixed models

Biometrika ◽

10.1093/biomet/asq042 ◽

2010 ◽

Vol 97 (4) ◽

pp. 773-789 ◽

Cited By ~ 127

Author(s):

Sonja Greven ◽

Thomas Kneib

Keyword(s):

Random Effects ◽

Mixed Models ◽

Linear Mixed Models ◽

Random Effect ◽

Computational Cost ◽

Information Criterion ◽

R Package ◽

Estimation Uncertainty ◽

Asymptotically Unbiased Estimator ◽

Selection Of

Abstract In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, aic, have been used, based either on the marginal or on the conditional distribution. We show that the marginal aic is not an asymptotically unbiased estimator of the Akaike information, and favours smaller models without random effects. For the conditional aic, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that can lead to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional aic, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package (R Development Core Team, 2010) is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

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Heat, Hills and the High Season: A Model-Based Comparative Analysis of Spatio-Temporal Factors Affecting Shared Bicycle Use in Three Southern European Islands

Sustainability ◽

10.3390/su13063274 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3274

Author(s):

Suzanne Maas ◽

Paraskevas Nikolaou ◽

Maria Attard ◽

Loukas Dimitriou

Keyword(s):

Correlation Analysis ◽

Mixed Models ◽

Linear Mixed Models ◽

Positive Association ◽

Sustainable Mobility ◽

Weather Factors ◽

Gran Canaria ◽

Bivariate Correlation ◽

Temporal Factors

Bicycle sharing systems (BSSs) have been implemented in cities worldwide in an attempt to promote cycling. Despite exhibiting characteristics considered to be barriers to cycling, such as hot summers, hilliness and car-oriented infrastructure, Southern European island cities and tourist destinations Limassol (Cyprus), Las Palmas de Gran Canaria (Canary Islands, Spain) and the Valletta conurbation (Malta) are all experiencing the implementation of BSSs and policies to promote cycling. In this study, a year of trip data and secondary datasets are used to analyze dock-based BSS usage in the three case-study cities. How land use, socio-economic, network and temporal factors influence BSS use at station locations, both as an origin and as a destination, was examined using bivariate correlation analysis and through the development of linear mixed models for each case study. Bivariate correlations showed significant positive associations with the number of cafes and restaurants, vicinity to the beach or promenade and the percentage of foreign population at the BSS station locations in all cities. A positive relation with cycling infrastructure was evident in Limassol and Las Palmas de Gran Canaria, but not in Malta, as no cycling infrastructure is present in the island’s conurbation, where the BSS is primarily operational. Elevation had a negative association with BSS use in all three cities. In Limassol and Malta, where seasonality in weather patterns is strongest, a negative effect of rainfall and a positive effect of higher temperature were observed. Although there was a positive association between BSS use and the number of visiting tourists in Limassol and Malta, this is predominantly explained through the multi-collinearity with weather factors rather than by intensive use of the BSS by tourists. The linear mixed models showed more fine-grained results and explained differences in BSS use at stations, including differences for station use as an origin and as a destination. The insights from the correlation analysis and linear mixed models can be used to inform policies promoting cycling and BSS use and support sustainable mobility policies in the case-study cities and cities with similar characteristics.

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