A guide for kernel generalized regression methods for genomic-enabled prediction

AbstractThe primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for capturing complex non-linear patterns that conventional linear regression models cannot. Furthermore, these methods are also powerful for leveraging environmental covariates, such as genotype × environment (G×E) prediction, among others. In this study we provide the building process of seven kernel methods: linear, polynomial, sigmoid, Gaussian, Exponential, Arc-cosine 1 and Arc-cosine L. Additionally, we highlight illustrative examples for implementing exact kernel methods for genomic prediction under a single-environment, a multi-environment and multi-trait framework, as well as for the implementation of sparse kernel methods under a multi-environment framework. These examples are followed by a discussion on the strengths and limitations of kernel methods and, subsequently by conclusions about the main contributions of this paper.

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Sparse kernel methods for high-dimensional survival data

Bioinformatics ◽

10.1093/bioinformatics/btn253 ◽

2008 ◽

Vol 24 (14) ◽

pp. 1632-1638 ◽

Cited By ~ 47

Author(s):

L. Evers ◽

C.-M. Messow

Keyword(s):

Survival Data ◽

Kernel Methods ◽

High Dimensional ◽

Sparse Kernel Methods ◽

Sparse Kernel

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Genomic prediction based on data from three layer lines using non-linear regression models

Genetics Selection Evolution ◽

10.1186/s12711-014-0075-3 ◽

2014 ◽

Vol 46 (1) ◽

Cited By ~ 5

Author(s):

Heyun Huang ◽

Jack J Windig ◽

Addie Vereijken ◽

Mario PL Calus

Keyword(s):

Linear Regression ◽

Genomic Prediction ◽

Regression Models ◽

Linear Regression Models ◽

Non Linear

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Automated selection of interaction effects in sparse kernel methods to predict pregnancy viability

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) ◽

10.1109/cidm.2013.6597213 ◽

2013 ◽

Cited By ~ 3

Author(s):

Vanya Van Belle ◽

Paulo Lisboa

Keyword(s):

Kernel Methods ◽

Interaction Effects ◽

Sparse Kernel Methods ◽

Sparse Kernel ◽

Selection Of

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Catch-effort maximum likelihood estimation of important population parameters

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f96-327 ◽

1997 ◽

Vol 54 (4) ◽

pp. 890-897 ◽

Cited By ~ 3

Author(s):

W R Gould ◽

K H Pollock

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Regression Models ◽

Model Development ◽

Likelihood Estimation ◽

Maximum Likelihood Estimates ◽

Linear Regression Models ◽

Estimation Of Parameters ◽

Regression Methods ◽

Regression Techniques

The relative ease with which linear regression models are understood explains the popularity of such techniques in estimating population size with catch-effort data. However, the development and use of the regression models require assumptions and approximations that may not accurately reflect reality. We present the model development necessary for maximum likelihood estimation of parameters from catch-effort data using the program SURVIV, the primary intent being to present biologists with a vehicle for producing maximum likelihood estimates in lieu of using the traditional regression techniques. The differences between the regression approaches and maximum likelihood estimation will be illustrated with an example of commercial fishery catch-effort data and through simulation. Our results indicate that maximum likelihood estimation consistently provides less biased and more precise estimates than the regression methods and allows for greater model flexibility necessary in many circumstances. We recommend the use of maximum likelihood estimation in future catch-effort studies.

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Specifics of logit and probit regression in education sciences - why wouldn't we use it?

Cypriot Journal of Educational Sciences ◽

10.18844/cjes.v15i6.5305 ◽

2020 ◽

Vol 15 (6) ◽

pp. 1557-1568

Author(s):

Sinisa Opic

Keyword(s):

Social Sciences ◽

Linear Regression ◽

Comparative Research ◽

Regression Models ◽

Linear Regression Models ◽

Probit Regression ◽

Regression Methods ◽

Logit Regression ◽

The Social ◽

Keywords Education

Regression is one of the dominant analysis methods used in the social sciences and educational sciences. There are different regression methods based on the type of research that is being conducted. The probit and logit regression models are regression methods which are being used recently by most researchers. However, their interpretations are not straightfoward and most researchers end up misinterepreting the results from the probit and logit regression models. This research therefore aims to examine the differences between the probit and logit models, in comparison with other linear regression models. Using a comparative research design, this study utilises resources from previous researchers, hence, the study took a form of a literature review. The results of this study is essential to educational and social sciences researchers who make use of the probit, logit and other regression methods. The research also explains why logit and probit should be used in place of other regression models. Keywords: education sciences; Linear regression; Logit; Probit; Regression

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Sparse kernel methods

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)34857-7 ◽

2003 ◽

Vol 36 (16) ◽

pp. 795-800

Author(s):

Steve R. Gunn

Keyword(s):

Kernel Methods ◽

Sparse Kernel Methods ◽

Sparse Kernel

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ANALISIS PENGARUH LUAS PANEN DAN PRODUKTIVITAS UBI KAYU DI KABUPATEN KEBUMEN DENGAN MENGGUNAKAN REGRESI LINIER BERGANDA

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2021.13.2.5011 ◽

2021 ◽

Vol 13 (2) ◽

pp. 127

Author(s):

Umi Muslimah ◽

Agus Sugandha

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Regression Models ◽

Staple Food ◽

Linear Regression Models ◽

Regression Methods ◽

Staple Foods ◽

Existing Data

Cassava are one of the staple foods in place of rice. However, today almost no one consumes cassava as a staple food substitute for rice. Indonesia is ranked third as the world's largest producer of cassava. Therefore, to maintain the value of yam production, the author will look for linear regression models as well as the best models with factors that are harvest area and productivity. Productivity is defined as the result of a comparison between the area of harvest and production. To search for regression models use multiple linear regression methods, while the best models use stepwise methods. Based on existing data, the best model is obtained with negative interception and influenced by productivity and the extent of the yam harvest.

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FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

Journal of Business Economics ◽

10.35760/eb.2018.v23i1.1812 ◽

2018 ◽

Vol 23 (1) ◽

pp. 60-71

Author(s):

Wigiyanti Masodah

Keyword(s):

Interest Rate ◽

Linear Regression ◽

Interest Rates ◽

Regression Models ◽

Linear Regression Models ◽

Negative Impacts ◽

Main Activity ◽

The Impact ◽

The Given ◽

Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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