Application of artificial intelligence based and multiple regression techniques for monthly precipitation modeling in coastal and inland stations

2020 ◽  
Vol 177 ◽  
pp. 338-349
Author(s):  
Jazuli Abdullahi ◽  
Anoosheh Iravanian ◽  
Vahid Nourani ◽  
Gozen Elkiran
Keyword(s):  
Artificial Intelligence ◽  
Multiple Regression ◽  
Monthly Precipitation ◽  
Regression Techniques ◽  
Precipitation Modeling

Some issues in the simulation of ANOVA by multiple regression techniques

1975 ◽  
Vol 5 (1) ◽  
pp. 199-207 ◽  
Author(s):  
John T. McArthur ◽  
Barry J. Fraser ◽  
Leo H. T. West
Keyword(s):  
Multiple Regression ◽  
Regression Techniques

scholarly journals The optimal number of partial least squares components in genomic selection for pork pH

2017 ◽  
Vol 47 (1) ◽  
Author(s):  
Fernanda Gomes da Silveira ◽  
Darlene Ana Souza Duarte ◽  
Lucas Monteiro Chaves ◽  
Fabyano Fonseca e Silva ◽  
Ivan Carvalho Filho ◽  
...  
Keyword(s):  
Least Squares ◽  
Genomic Selection ◽  
Multiple Regression ◽  
Partial Least Squares ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Theoretical Background ◽  
Optimal Number ◽  
Number Of Components ◽  
Regression Techniques

ABSTRACT: The main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort.

Heredity and Environment in Phoneme Articulation: Hereditary and Environmental Contributions to Articulation Proficiency

1995 ◽  
Vol 44 (2) ◽  
pp. 63-73
Author(s):  
W. E. Dixon ◽  
A. P. Matheny ◽  
S. R. Mohr
Keyword(s):  
Factor Analysis ◽  
Multiple Regression ◽  
Behavioral Genetics ◽  
Environmental Influences ◽  
Individual Factors ◽  
Factor Scores ◽  
Genetic Components ◽  
Sources Of Variation ◽  
Regression Techniques ◽  
The Individual

AbstractLarge twin samples and recent applications of multiple regression techniques to behavioral genetics methodology makes possible evaluation of genetic and environmental contributions to the articulation proficiency of individual phonemes. Factor analysis of the articulation scores from 256 MZ and DZ twins and 124 of their non-twin siblings (all children ranged from 2; 11 to 9; 8 years) were conducted to reduce a 50-item articulation test to a more manageable set of five articulation factors. The twins' factor scores were then analyzed using multiple regression procedures to determine the extent to which the individual factors resulted from genetic and/or environmental influences. The /r/ and /∫, t∫, dƺ/ factors were found to have strong genetic components, while the /l, j, w/ factor was found to be strongly influenced by environmental sources of variation.

Identification of Twins by Spoken Syllables

2001 ◽  
Vol 92 (1) ◽  
pp. 8-10
Author(s):  
Wim A. Van Dommelen
Keyword(s):  
Multiple Regression ◽  
Speaker Identification ◽  
Regression Techniques

It is shown that the results of an experiment on speaker identification described by Whiteside and Rixon in 2000 seem to be contradictory and inconclusive. To investigate whether the experiment allows reliable conclusions, re-evaluation of their data using multiple regression techniques is proposed.

The reduction of nutrient digestibility and energy concentration from maintenance feeding (sheep) to production feeding (lactating dairy cows) in grass silage-based diets

2002 ◽  
Vol 2002 ◽  
pp. 113-113
Author(s):  
T. Yan ◽  
C. P. Ferris ◽  
R. E. Agnew ◽  
F. J. Gordon
Keyword(s):  
Dairy Cows ◽  
Multiple Regression ◽  
Nutrient Digestibility ◽  
Energy Concentration ◽  
Feeding Level ◽  
Grass Silage ◽  
Lactating Dairy Cows ◽  
Unit Increase ◽  
Regression Techniques

AFRC (1993) recommends a reduction of proportionately 0.018 in dietary metabolisble energy (ME) concentration with each unit increase in feeding level above maintenance in dairy cows (feeding level is calculated as total ME intake divided by ME requirement for maintenance). A similar value (0.016) was reported recently by Yan et al. (2001) using a number of linear and multiple regression techniques with lactating dairy cows offered grass silage-based diets. The objectives of the present study were to validate these two values and also to evaluate the effects of feeding level on nutrient digestibility and ME concentration in the mixed diets.

Spatiotemporal precipitation modeling by artificial intelligence-based ensemble approach

2019 ◽  
Vol 79 (1) ◽  
Author(s):  
Vahid Nourani ◽  
Nazanin Behfar ◽  
Selin Uzelaltinbulat ◽  
Fahreddin Sadikoglu
Keyword(s):  
Artificial Intelligence ◽  
Ensemble Approach ◽  
Precipitation Modeling
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