• Title/Summary/Keyword: Selection Response

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Response to Selection for Milk Yield and Lactation Length in Buffaloes

  • Khan, M.S.
    • Asian-Australasian Journal of Animal Sciences
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    • v.10 no.6
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    • pp.567-570
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    • 1997
  • A multiple trait animal model having milk yield and lactation length was used to estimate genetic parameters using data from four institutional herds and four field recording centers. Response to selection for milk yield alone and in combination with lactation length was estimated by using principles of genetic theory. Lactation records (n = 2,353) adjusted for age at calving to 60 months were utilized. Milk yield was 17% heritable with repeatability of 0.44. Lactation length had a low heritability of 0.06 with repeatability of 0.16. Genetic correlation between the two traits was 0.70. Selection response in milk yield can be improved slightly (103.8 vs 102.8 kg) when information on covariance with lactation length is used together with the information on milk yield.

The Response of QTL in Generation during Selection (선발과정에서의 세대별 QTL 좌위 고정에 관한 연구)

  • Lee Ji-Woong
    • Journal of Embryo Transfer
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    • v.20 no.3
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    • pp.217-232
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    • 2005
  • The objective of this study was to determine the response of QTL in each generation during selection to develop inbred lines. The simulation program was written in Fortran. Magnitude of QTL effects, base population size, number of QTL assigned to population, and the allelic frequency for the positive allele at each major QTL were highly associated with number of generations to fixation of QTLs during selection. Populations with larger QTL effects and larger base population size had more individuals with fixed QTL. However, a smaller number of QTL assigned to population had a higher fraction of individuals with fixed QTL at each generation compared with more populations with QTL. This simulation study will help to design biological experiments for detection of QTL-marker association using inbred population and to determine optimum number of lines with fixed QTL during inbred line development. To complement this study, additional simulation should be need with abundant replicates, more various population sizes, magnitude of QTL effects, and recombination between markers and QTLs.

Model selection method for categorical data with non-response (무응답을 가지고 있는 범주형 자료에 대한 모형 선택 방법)

  • Yoon, Yong-Hwa;Choi, Bo-Seung
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.4
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    • pp.627-641
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    • 2012
  • We consider a model estimation and model selection methods for the multi-way contingency table data with non-response or missing values. We also consider hierarchical Bayesian model in order to handle a boundary solution problem that can happen in the maximum likelihood estimation under non-ignorable non-response model and we deal with a model selection method to find the best model for the data. We utilized Bayes factors to handle model selection problem under Bayesian approach. We applied proposed method to the pre-election survey for the 2004 Korean National Assembly race. As a result, we got the non-ignorable non-response model was favored and the variable of voting intention was most suitable.

An Iterative Posterior Preference Articulation Approach to Dual Response Surface Optimization (쌍대반응표면최적화를 위한 반복적 선호도사후제시법)

  • Jeong, In-Jun
    • Journal of Korean Society for Quality Management
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    • v.40 no.4
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    • pp.481-496
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    • 2012
  • Purpose: This paper aims at improving inefficiency of an existing posterior preference articulation method proposed for dual response surface optimization. The method generates a set of non-dominated solutions and then allows a decision maker (DM) to select the best solution among them through an interval selection strategy. Methods: This paper proposes an iterative posterior preference articulation method, which repeatedly generates the predetermined number of non-dominated solutions in an interval which becomes gradually narrower over rounds. Results: The existing method generates a good number of non-dominated solutions not used in the DM's selection process, while the proposed method generates the minimal number of non-dominated solutions necessitated in the selection process. Conclusion: The proposed method enables a satisfactory compromise solution to be achieved with minimal cognitive burden of the DM as well as with light computation load in generating non-dominated solutions.

A Bayesian Method for Narrowing the Scope fo Variable Selection in Binary Response t-Link Regression

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • v.29 no.4
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    • pp.407-422
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    • 2000
  • This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

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The Relationship between scuba diving participant's selective attribute, emotional response, and empirical value

  • Lee, Yoo-Chan;Jung, Sang-Ok
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.3
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    • pp.84-91
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    • 2021
  • The purpose of this study is to investigate the structural relationship between resort selection attributes, emotional responses, and empirical values of scuba diving participants. The general population who enjoys scuba diving in Korea was selected as the population. Using the convenience sampling method, 553 of the 600 questionnaire samples were extracted as the final valid sample. For data processing, frequency analysis, exploratory factor analysis, and Cronbach's α test were performed using SPSS 23, and confirmatory factor analysis and structural equation model analysis were performed with AMOS 18. The results are as follows: First, among the sub-factors of selection attributes, equipment, facility environment, and diving point showed a positive effect on emotional response, but staff service did not have any significant effect. Second, the emotional response positively affected by the selection attribute showed a positive effect on all factors of service excellence, consumer utility, fun value, and aesthetic value of empirical value. Therefore, scuba diving resort managers must recognize the importance of equipment, facility environment, and diving point among these selection attributes of customers. And to satisfy the customer needs the resort must accurately identify the needs for diving equipment, facility environment and diving point. Various methods for this should be explored through the needs of the identified customers, and efforts should be made to provide safe equipment, comfortable facilities, and various diving points.

Responses in Partial, Residual and Annual Egg Production Expected from Selection on Part Record in Synthetic White Leghorn flock (산란계의 합성종계통에 있어서 부분검정에 의한 선발효과 추정에 관한 연구)

  • 오봉국;이정구;이문연
    • Korean Journal of Poultry Science
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    • v.9 no.1
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    • pp.35-42
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    • 1982
  • Data pertaining to the first generation of a Synthetic White Leghorn flock were used to estimate the heritabilities of and genetic correlation between partial egg production(egg number or percentage) or diversely segmented part records and other traits such as age at sexual maturity, residual and annual egg production, and to compare the expected genetic gain from selection on partial egg number or partial percent production with correlated response in other traits. The estimated heritabilites for six measures of egg Production were ranged from 29 to 35, while heritability for age at sexual maturity (SM) was intermediate (48). Genetic correlations between partial egg number (P) and annual egg number. (A), and partial percent production (P') and annual percent production (A') were 51 and 72, respectively. Genetic correlation between P and SM was estimated largely negative (-.64), while correlation bettween P' and SM was positively intermediate(34). In comparing direct response from selection on partial production (P or P') with another response in correlated trait, selection on P would be 25% more efficient than selection of P' for improving A, while selection of P' would be 94% more efficient than selection P for improving A' For shortening SM selection of P would be 98% more efficient than selection on P'.

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A Posterior Preference Articulation Method to Dual-Response Surface Optimization: Selection of the Most Preferred Solution Using TOPSIS (쌍대반응표면최적화를 위한 사후선호도반영법: TOPSIS를 활용한 최고선호해 선택)

  • Jeong, In-Jun
    • Knowledge Management Research
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    • v.19 no.2
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    • pp.151-162
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    • 2018
  • Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

Optimal Voltage Vector Selection Method for Torque Ripple Reduction in the Direct Torque Control of Five-phase Induction Motors

  • Kang, Seong-Yun;Shin, Hye Ung;Park, Sung-Min;Lee, Kyo-Beum
    • Journal of Power Electronics
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    • v.17 no.5
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    • pp.1203-1210
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    • 2017
  • This paper presents an improved switching selection method for the direct torque control (DTC) of five-phase induction motors (IMs). The proposed method is conducted using optimal switching selection. A five-phase inverter has 32 voltage vectors which are divided into 30 nonzero voltage vectors and two zero voltage vectors. The magnitudes of the voltage vectors consist of large, medium, and small voltage vectors. In addition, these vectors are related to the torque response and torque ripple. When a large voltage vector is selected in a drive system, the torque response time decreases with an increased torque ripple. On the other hand, when a small voltage vector is selected, the torque response time and torque ripple increase. As a result, this paper proposes an optimal voltage vector selection method for improved DTC of a five-phase induction machine depending on the situation. Simulation and experimental results verify the effectiveness of the proposed control algorithm.

Evaluation of Optimum Genetic Contribution Theory to Control Inbreeding While Maximizing Genetic Response

  • Oh, S.H.
    • Asian-Australasian Journal of Animal Sciences
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    • v.25 no.3
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    • pp.299-303
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    • 2012
  • Inbreeding is the mating of relatives that produce progeny having more homozygous alleles than non-inbred animals. Inbreeding increases numbers of recessive alleles, which is often associated with decreased performance known as inbreeding depression. The magnitude of inbreeding depression depends on the level of inbreeding in the animal. Level of inbreeding is expressed by the inbreeding coefficient. One breeding goal in livestock is uniform productivity while maintaining acceptable inbreeding levels, especially keeping inbreeding less than 20%. However, in closed herds without the introduction of new genetic sources high levels of inbreeding over time are unavoidable. One method that increases selection response and minimizes inbreeding is selection of individuals by weighting estimated breeding values with average relationships among individuals. Optimum genetic contribution theory (OGC) uses relationships among individuals as weighting factors. The algorithm is as follows: i) Identify the individual having the best EBV; ii) Calculate average relationships ($\bar{r_j}$) between selected and candidates; iii) Select the individual having the best EBV adjusted for average relationships using the weighting factor k, $EBV^*=EBV_j(1-k\bar{{r}_j})$ Repeat process until the number of individuals selected equals number required. The objective of this study was to compare simulated results based on OGC selection under different conditions over 30 generations. Individuals (n = 110) were generated for the base population with pseudo random numbers of N~ (0, 3), ten were assumed male, and the remainder female. Each male was mated to ten females, and every female was assumed to have 5 progeny resulting in 500 individuals in the following generation. Results showed the OGC algorithm effectively controlled inbreeding and maintained consistent increases in selection response. Difference in breeding values between selection with OGC algorithm and by EBV only was 8%, however, rate of inbreeding was controlled by 47% after 20 generation. These results indicate that the OGC algorithm can be used effectively in long-term selection programs.