<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper analyzes the relationship between talent introduction policy and talent mobility based on a logistic breakpoint regression model. The logit function and likelihood function are analyzed to determine the probability values between the relationship of things. The variable selection is achieved by adding penalty terms in estimating the regression parameters. Focused on the analysis of the MM algorithm to select the optimal control function and iterative solution to separate the parameters in the optimization problem. The processing effect of breakpoint analysis is studied, and breakpoint regression is classified and used to optimize the objective function to the minimum using local linear regression. The feasibility of the breakpoint analysis method is demonstrated by analyzing the use of breakpoint regression in different fields. The results surface: the probability of implementing a talent introduction policy for individuals on one side of the breakpoint is 0, while the probability on the other side of the breakpoint is 1, which meets the condition of exact breakpoint regression. The most talent policies are mainly concentrated in the third and fourth tier cities, accounting for 49%, followed by the second tier cities, with 39% of talent introduction policies.</p>
</abstract>

This paper analyzes the relationship between talent introduction policy and talent mobility based on a logistic breakpoint regression model. The logit function and likelihood function are analyzed to determine the probability values between the relationship of things. The variable selection is achieved by adding penalty terms in estimating the regression parameters. Focused on the analysis of the MM algorithm to select the optimal control function and iterative solution to separate the parameters in the optimization problem. The processing effect of breakpoint analysis is studied, and breakpoint regression is classified and used to optimize the objective function to the minimum using local linear regression. The feasibility of the breakpoint analysis method is demonstrated by analyzing the use of breakpoint regression in different fields. The results surface: the probability of implementing a talent introduction policy for individuals on one side of the breakpoint is 0, while the probability on the other side of the breakpoint is 1, which meets the condition of exact breakpoint regression. The most talent policies are mainly concentrated in the third and fourth tier cities, accounting for 49%, followed by the second tier cities, with 39% of talent introduction policies.

Analysis of the Economics of Talent Mobility Based on Logistic Model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Analysis of the Economics of Talent Mobility Based on Logistic Model"}

This paper analyzes the relationship between talent introduction policy and talent mobility based on a logistic breakpoint regression model. The logit function...

Analysis of the Economics of Talent Mobility Based on Logistic Model

Publicado en línea: 17 oct 2023

Páginas: -

Recibido: 13 ene 2023

Aceptado: 27 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00672

Palabras clave
Logistic model, MM algorithm, Variable selection, Breakpoint regression, Talent mobility

© 2023 Liping Sun, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the Economics of Talent Mobility Based on Logistic Model

Publicado en línea: 17 oct 2023

Páginas: -

Recibido: 13 ene 2023

Aceptado: 27 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00672

Palabras claveLogistic model, MM algorithm, Variable selection, Breakpoint regression, Talent mobility

© 2023 Liping Sun, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Logistic model, MM algorithm, Variable selection, Breakpoint regression, Talent mobility