• Title, Summary, Keyword: logistic curve

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Goodness-of-fit test for the half logistic distribution based on multiply Type-II censored samples

  • Kang, Suk-Bok;Cho, Young-Seuk;Han, Jun-Tae;SaKong, Jin
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.2
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    • pp.317-325
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    • 2010
  • In this paper, we develop four modified empirical distribution function (EDF) type tests using approximate maximum likelihood estimators for the half-logistic distribution based on multiply Type-II censored samples. We also propose modified normalize sample Lorenz curve polt and new test statistics. We compare the above test statistics in the sense of the power for various censored samples. We present an example to illustrate this method.

An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain

  • Park, Hyeoun-Ae
    • Journal of Korean Academy of Nursing
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    • v.43 no.2
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    • pp.154-164
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    • 2013
  • Purpose: The purpose of this article is twofold: 1) introducing logistic regression (LR), a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, and 2) examining use and reporting of LR in the nursing literature. Methods: Text books on LR and research articles employing LR as main statistical analysis were reviewed. Twenty-three articles published between 2010 and 2011 in the Journal of Korean Academy of Nursing were analyzed for proper use and reporting of LR models. Results: Logistic regression from basic concepts such as odds, odds ratio, logit transformation and logistic curve, assumption, fitting, reporting and interpreting to cautions were presented. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, sample size was not sufficiently large to call into question the accuracy of the regression model. Additionally, only one study reported validation analysis. Conclusion: Nursing researchers need to pay greater attention to guidelines concerning the use and reporting of LR models.

Mesh Selectivity of Durm Net Fish Trap for Elkhorn sculpin(Alcichthys alcicornis) in the Eastern Sea of Korea (동해의 장구형 통발에 대한 빨간횟대 (Alcichthys alcicornis)의 망목선택성)

  • Park, Hae-Hoon;Jeong, Eui-Cheol;An, Heui-Chun;Park, Chang-Doo;Kim, Hyun-Young;Bae, Jae-Hyun;Cho, Sam-Kwang;Baik, Chul-In
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.40 no.4
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    • pp.247-254
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    • 2004
  • The mesh selectivity of the drum net fish trap for elkhorn sculpin(Alcichthys alcicornis) in the estern sea of Korea was described. The selection curve for the elkhorn sculpin caught from the experiments between June 2003 and December 2003 was by SELECT(Share Each Length Class's Catch Total)model and by Kitahaa's method to a polynomial equation and two parameter logistic selection curve. The selection curve by SELECT model showed to be equal probability of entrance of the elkhorn sculpin in the large(55mm) and small(20mm) mesh traps by minimum AIC (Akaike Information Criteria). The equation of selectivity curve obtained by Kitahara's method using a logistic function with least square method was $s(R)\;=\;\frac{1}{1+exp(-0.3545R+2.141)$, where R=1/m, and/and m are total length and mesh size, respectively. The mesh selectivity curve showed that the current regulated mesh size(35mm) for the trap was corresponded to 21.4cm in the $L_{50}$of the selection curve for the elkhorn sculpin.

Factors Affecting Growth Curve Parameters of Hanwoo Cows (한우 암소의 성장곡선 모수에 영향을 미치는 요인)

  • Lee, C.W.;Choi, J.G.;Jeon, K.J.;Na, K.J.;Lee, C.;Hwang, J.M.;Kim, J.B.
    • Journal of Animal Science and Technology
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    • v.45 no.5
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    • pp.711-724
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    • 2003
  • Some growth curve models were used to fit individual growth of 1,083 Hanwoo cows born from 1970 to 2001 in Daekwanryeong branch, National Livestock Research Institute(NLRI). The effects of year-season of birth and age of dam were analyzed. In analysis of variance for growth curve parameters, the effects of birth year-season were significant for mature weight(A), growth ratio(b) and maturing rate(k)(P〈.01). The effects of age of dam were significant for growth ratio(b) but not significant for mature weight(A) and maturing rate(k). The linear term of the covariate of age at the final weights was significant for the A(P〈.01) and k(P〈.01) of Gompertz model, von Bertalanffy model and Logistic model. For the growth curve parameters fitted on individual data using Gompertz model, von Bertalanffy model and Logistic model, resulting the linear contrasts(fall-spring), Least square means of A in three nonlinear models were higher cows born at fall and A of Logistic model was significant(P〈.05) between the seasons. According to the results of the least square means of growth curve parameters by age of dam, least square means of mature weight(A) in Gompertz model was largest in 6 year and smallest estimating for 3 and 8 years of age of dam. The growth ratio(b) was largest in 2 year of age of dam and smallest estimating in 8 year. The A and k were not different by age of dam(p〉.05), On the other hand, the b was different by age of dam(p〈.01). The estimate of A in von Bertalanffy model was largest in 6 year and smallest in 8 and 9 years of age of dam. The b was largest in 2 year and tend to decline as age of dam increased. The A and k were not different by age of dam(p〉.05), On the other hand, the b was highly significant by age of dam(p〈.01).

Estimation of heritability and genetic correlation of body weight gain and growth curve parameters in Korean native chicken

  • Manjula, Prabuddha;Park, Hee-Bok;Seo, Dongwon;Choi, Nuri;Jin, Shil;Ahn, Sung Jin;Heo, Kang Nyeong;Kang, Bo Seok;Lee, Jun-Heon
    • Asian-Australasian Journal of Animal Sciences
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    • v.31 no.1
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    • pp.26-31
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    • 2018
  • Objective: This study estimated the genetic parameters for body weight gain and growth curve parameter traits in Korean native chicken (KNC). Methods: A total of 585 $F_1$ chickens were used along with 88 of their $F_0$ birds. Body weights were measured every 2 weeks from hatching to 20 weeks of age to measure weight gain at 2-week intervals. For each individual, a logistic growth curve model was fitted to the longitudinal growth dataset to obtain three growth curve parameters (${\alpha}$, asymptotic final body weight; ${\beta}$, inflection point; and ${\gamma}$, constant scale that was proportional to the overall growth rate). Genetic parameters were estimated based on the linear-mixed model using a restricted maximum likelihood method. Results: Heritability estimates of body weight gain traits were low to high (0.057 to 0.458). Heritability estimates for ${\alpha}$, ${\beta}$, and ${\gamma}$ were $0.211{\pm}0.08$, $0.249{\pm}0.09$, and $0.095{\pm}0.06$, respectively. Both genetic and phenotypic correlations between weight gain traits ranged from -0.527 to 0.993. Genetic and phenotypic correlation between the growth curve parameters and weight gain traits ranged from -0.968 to 0.987. Conclusion: Based on the results of this study population, we suggest that the KNC could be used for selective breeding between 6 and 8 weeks of age to enhance the overall genetic improvement of growth traits. After validation of these results in independent studies, these findings will be useful for further optimization of breeding programs for KNC.

Gaussian Response Curves Fitting for Ecology Data (생태학 자료에 대한 가우시안 반응곡선 적합)

  • Jeong, Hyeong Chul
    • Journal of the Korean Data Analysis Society
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    • v.20 no.5
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    • pp.2307-2318
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    • 2018
  • In this study, the Gaussian response regression and Gaussian logistics regression to describe the species occurrence along the environmental gradients is considered. In ecology, the occurrence of species is generally influenced by environmental variables and follows the Gaussian curve form. Using Gaussian curves, optimal environmental conditions, tolerance of environmental factors, peak abundance or peak abundance probability can be estimated for the expression of the species. In this study, Poisson regression or logistic regression analysis, which is a generalized linear model, is used to estimate the Gaussian curve. These models are applied to the spider data of Aart, Smeenk (1975) and the occurrence of the spider, the probability of occurrence of the spider are estimated on the environmental gradients. However, it is not possible to estimate the Gaussian curve unless the species abundance is followed by a uni-modal distribution. In this study, the univariate Gaussian response curve estimation is limited by the analysis with one environmental variable.

Forecasting methodology of future demand market (미래 수요시장의 예측 방법론)

  • Oh, Sang-young
    • Journal of Digital Convergence
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    • v.18 no.2
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    • pp.205-211
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    • 2020
  • The method of predicting the future may be predicted by technical characteristics or technical performance. Therefore, technology prediction is used in the field of strategic research that can produce economic and social benefits. In this study, we predicted the future market through the study of how to predict the future with these technical characteristics. The future prediction method was studied through the prediction of the time when the market occupied according to the demand of special product. For forecasting market demand, we proposed the future forecasting model through comparison of representative quantitative analysis methods such as CAGR model, BASS model, Logistic model and Gompertz Growth Curve. This study combines Rogers' theory of innovation diffusion to predict when products will spread to the market. As a result of the research, we developed a methodology to predict when a particular product will mature in the future market through the spread of various factors for the special product to occupy the market. However, there are limitations in reducing errors in expert judgment to predict the market.

Estimation of Growth Curve for Evaluation of Growth Characteristics for Hanwoo cows (한우암소의 성장특성 평가를 위한 성장곡선의 추정)

  • Lee, C.W.;Choi, J.G.;Jeon, K.J.;Na, K.J.;Lee, C.;Yang, B.K.;Kim, J.B.
    • Journal of Animal Science and Technology
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    • v.45 no.4
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    • pp.509-516
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    • 2003
  • Growth curves were estimated for 1083 female Korean cattle raised in Daekwanryeong branch, National Livestock Research Institute (NLRI). Comparisons were made among various growth curve models for goodness of fit for the growth of the cows. Estimated growth curve functions were $W_t=370.2e^{-2.208e^{-0.00327t}$ for Gompertz model, for von Bertalanffy model, and $W_t=341.2(1+5.652e^{-0.00524t})^{-1}$ for Logistic model. Ages at inflection estimated from Gompertz model, von Bertalanffy model and Logistic model were 242.2 days, 191.5 days, and 330.5 days respectively, body weight at inflection were 136kg, 115kg, and 170kg, and daily gain at inflection were 0.445kg, 0.451kg, and 0.446kg. The predicted weights by ages from Gompertz model, von Bertalanffy model, and Logistic model were onsistently overestimated at birth weight and underestimated at 36 month weight. The von Bertalanffy model which had a variable point of inflection fit the data best.

Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

  • Lee, Junghoon;Jung, Ho-Yeon;Koo, J.R.;Yoon, Yoonjin;Jung, Hyung-Jo
    • Journal of Electrical Engineering and Technology
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    • v.12 no.2
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    • pp.969-980
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    • 2017
  • Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.

Landslide susceptibility mapping using Logistic Regression and Fuzzy Set model at the Boeun Area, Korea (로지스틱 회귀분석과 퍼지 기법을 이용한 산사태 취약성 지도작성: 보은군을 대상으로)

  • Al-Mamun, Al-Mamun;JANG, Dong-Ho
    • Journal of The Geomorphological Association of Korea
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    • v.23 no.2
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    • pp.109-125
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    • 2016
  • This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.