Examination of Distance Based Regression Methods for Different Data Structures in Animal Science
Year: 2025
Type: article
Abstract: Distance-based regression is an alternative method for parameter estimation in linear regression models when mixed-type explanatory variables are used. Distance-based regression is similar to classical linear regression, except that explanatory variables are measured by distance measures rather than raw values. In this study, datasets with sample sizes of 10, 25, 50, 100, 250 and 500 produced for Binomial, Normal, t, Chi-square and Poisson distributions of Euclidean, Gower and Manhattan distance measures and real data with discrete and continuous distribution that body weight at sixth months was used as outcome variable, body length and chest depth at sixth months of Saanen kids were used as explanatory variables as continuous data. Milk fat ratio was determined as the response variable, while the number of milking per day and the season of Polish Holstein Friesian cattle were determined as the explanatory variables as discrete data. It was aimed to determine the effect on the data sets (10, 50 and 100 sample sizes) by comparing the results obtained from the Linear Regression method. R packages "dbstats", "cluster" and "tidyverse" were used to perform the analysis. As a result, it has been determined that the use of Manhattan distance in data with Poisson distribution may produce unsuccessful results, especially in small sample sizes (n
Authors: Burcu Kurnaz, Hasan Önder
Institution: Ondokuz Mayıs University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408039283