Results 51 to 60 of about 194,005 (326)

Henderson's method approach to Kernel prediction in partially linear mixed models

Cumhuriyet Science Journal, 2020
In this article, we propose Kernel prediction in partially linear mixed models by using Henderson's method approach. We derive the Kernel estimator and the Kernel predictor via the mixed model equations (MMEs) of Henderson's that they give the best ...
Seçil Yalaz, Özge Kuran
doaj   +1 more source

Predicted Residual Error Sum of Squares of Mixed Models: An Application for Genomic Prediction. [PDF]

, 2017
Genomic prediction is a statistical method to predict phenotypes of polygenic traits using high-throughput genomic data. Most diseases and behaviors in humans and animals are polygenic traits. The majority of agronomic traits in crops are also polygenic.
Xu, Shizhong
core   +2 more sources

Genomic selection in rubber tree breeding: A comparison of models and methods for managing G×E interactions [PDF]

, 2019
Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant
Francisco, Felipe O., Fritsche-Neto, Roberto, Gonçalves, Paulo De Souza, Le Guen, Vincent, Scaloppi Junior, Erivaldo José, Souza, Anete Pereira, Souza, Livia Moura   +6 more
core   +2 more sources

Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale [PDF]

, 2014
Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change.
Arrouays, D., Boulonne, L., Jolivet, C., Lacarce, E., Martin, M. P., Meersmans, J., Orton, T. G., Paroissien, J. B., Saby, N. P. A.   +8 more
core   +3 more sources

Simultaneous prediction in the generalized linear model

Open Mathematics, 2018
This paper studies the prediction based on a composite target function that allows to simultaneously predict the actual and the mean values of the unobserved regressand in the generalized linear model.
Bai Chao, Li Haiqi
doaj   +1 more source

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

Frontiers in Psychiatry, 2020
We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images.
B. Couvy-Duchesne, Johann Faouzi, Benoît Martin, Elina Thibeau-Sutre, Adam Wild, M. Ansart, S. Durrleman, D. Dormont, Ninon Burgos, O. Colliot   +9 more
semanticscholar   +1 more source

Modelling the redshift-space distortion of galaxy clustering [PDF]

, 1997
We use a set of large, high-resolution cosmological N-body simulations to examine the redshift-space distortions of galaxy clustering on scales of order 10-200h^{-1} Mpc.
Cole, S., Hatton, S. J.
core   +2 more sources

Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models

, 2008
Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems.
Chatterjee, Snigdhansu, Lahiri, Partha, Li, Huilin   +2 more
core   +3 more sources

Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle. [PDF]

, 2017
BackgroundGenomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-
Cheng, Hao, Dekkers, Jack, Fernando, Rohan, Garrick, Dorian, Golden, Bruce, Lee, Deukhwan, Lee, Joonho, Park, Kyungdo   +7 more
core   +3 more sources

Genetic improvement of silage maize: predicting genetic gain using selection indexes and best linear unbiased prediction

Revista Ciência Agronômica, 2019
The objective of this study was to evaluate four selection indexes and best linear unbiased prediction (BLUP) for predicting genetic gain in maize hybrids used for silage. The genetic gain was compared between four selection indexes and BLUP.
Jocarla Ambrosim Crevelari, M. G. Pereira, Flávio Henrique Vidal Azevedo, R. Vieira   +3 more
semanticscholar   +1 more source