• Title/Summary/Keyword: logistic model

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Probability Estimation of Snow Damage on Sugi (Cryptomeria japonica) Forest Stands by Logistic Regression Model in Toyama Prefecture, Japan

  • Kamo, Ken-Ichi;Yanagihara, Hirokazu;Kato, Akio;Yoshimoto, Atsushi
    • Journal of Forest and Environmental Science
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    • v.24 no.3
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    • pp.137-142
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    • 2008
  • In this paper, we apply a logistic regression model to the data of snow damage on sugi (Cryptomeria japonica) occurred in Toyama prefecture (in Japan) in 2004 for estimating the risk probability. In order to specify the factors effecting snow damage, we apply a model selection procedure determining optimal subset of explanatory variables. In this process we consider the following 3 information criteria, 1) Akaike's information criterion, 2) Baysian information criterion, 3) Bias-corrected Akaike's information criterion. For the selected variables, we give a proper interpretation from the viewpoint of natural disaster.

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Bayesian inference of the cumulative logistic principal component regression models

  • Kyung, Minjung
    • Communications for Statistical Applications and Methods
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    • v.29 no.2
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    • pp.203-223
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    • 2022
  • We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

Forecasting Probability of Precipitation Using Morkov Logistic Regression Model

  • Park, Jeong-Soo;Kim, Yun-Seon
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.1-9
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    • 2007
  • A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow's precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.

Developing a Combined Forecasting Model on Hospital Closure (병원도산의 예측모형 개발연구)

  • 정기택;이훈영
    • Health Policy and Management
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    • v.10 no.2
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    • pp.1-21
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    • 2000
  • This study reviewde various parametic and nonparametic method for forexasting hospital closures in Korea. We compared multivariate discriminant analysis, multivartiate logistic regression, classfication and regression tree, and neural network method based on hit ratio of each model for forecasting hospital closure. Like other studies in the literture, neural metwork analysis showed highest average hit ratio. For policy and business purposes, we combined the four analytical method and constructed a foreasting model that can be easily used to predict the probabolity of hospital closure given financial information of a hospital.

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MULTIPLE OUTLIER DETECTION IN LOGISTIC REGRESSION BY USING INFLUENCE MATRIX

  • Lee, Gwi-Hyun;Park, Sung-Hyun
    • Journal of the Korean Statistical Society
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    • v.36 no.4
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    • pp.457-469
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    • 2007
  • Many procedures are available to identify a single outlier or an isolated influential point in linear regression and logistic regression. But the detection of influential points or multiple outliers is more difficult, owing to masking and swamping problems. The multiple outlier detection methods for logistic regression have not been studied from the points of direct procedure yet. In this paper we consider the direct methods for logistic regression by extending the $Pe\tilde{n}a$ and Yohai (1995) influence matrix algorithm. We define the influence matrix in logistic regression by using Cook's distance in logistic regression, and test multiple outliers by using the mean shift model. To show accuracy of the proposed multiple outlier detection algorithm, we simulate artificial data including multiple outliers with masking and swamping.

Biplots of Multivariate Data Guided by Linear and/or Logistic Regression

  • Huh, Myung-Hoe;Lee, Yonggoo
    • Communications for Statistical Applications and Methods
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    • v.20 no.2
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    • pp.129-136
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    • 2013
  • Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.

Forecasting the Diffusion of Technology using Patent Information: Focused on Information Security Technology for Network-Centric Warfare (특허정보를 활용한 기술 확산 예측: NCW 정보보호기술을 중심으로)

  • Kim, Do-Hoe;Park, Sang-Sung;Shin, Young-Geun;Jang, Dong-Sik
    • The Journal of the Korea Contents Association
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    • v.9 no.2
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    • pp.125-132
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    • 2009
  • The paradigm of economy has been transformed into knowledge based economic paradigm in 21th century. Analysis of patent trend is one of the strategic methods for increasing their patent competitive power. However, this method is just presenting statistical data about patent trend or qualitative analysis about some core technology. In this paper, we forecast technology diffusion using patent information for more progressive analysis. We make an experiment with bass model and logistic model and make use of patent data about information-security technology for NCW as input data. We conclude that the logistic model is more efficient for forecasting and this technology is approaching to the age of technology maturity.

INTRODUCTION TO DIFFUSIVE LOGISTIC EQUATIONS IN POPULATION DYNAMICS

  • Taira, Kazuaki
    • Journal of applied mathematics & informatics
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    • v.9 no.2
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    • pp.459-517
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    • 2002
  • The purpose of this paper is to provide a careful and accessible exposition of diffusive logistic equations with indefinite weights which model population dynamics in environments with strong spatial heterogeneity. We prove that the most favorable situations will occur if there is a relatively large favorable region (with good resources and without crowding effects) located some distance away from the boundary of the environment. Moreover we prove that a population will grow exponentially until limited by lack of available resources if the diffusion rate is below some critical value; this idea is generally credited to Thomas Malthus. On the other hand, if the diffusion rate is above this critical value, then the model obeys the logistic equation introduced by P. F. Verhulst .

A Study on general logistic center of agriculture products for location selection model (농산물 종합물류센터조성을 위한 입지선정 평가요인 분석)

  • 김규창
    • Journal of Distribution Research
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    • v.3 no.1
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    • pp.145-158
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    • 1998
  • The selection of proposed sites for the general logistic center of agriculture products would be made the most suitable place by considering the spread of population as real consumers, the prospect of the demand, the expansion of traffic system, the regional, hourly and carring traffic volume and the use of land based urban planning, etc. As the preconsideration, the possible occupant companies have to be selected on the category of business and the district. After posing questions and having interview, several selected regions would be compared and analysed for deciding the most suitable place. The model for the general logistic center of agricultural products must be selected taking key factors approach for choosing key factors at first and referring to many documentary records. And the more, cooperating with the specialists for location selection and making objective questions to concerned companies, the most suitable place is selected by marking high score for the moderate land cost, the low traffic jam, the connection with the back cities and the possible expansion as the general logistic center of agriculture products.

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Estimation of Logistic Regression for Two-Stage Case-Control Data (2단계 사례-대조자료를 위한 로지스틱 회귀모형의 추론)

  • 신미영;신은순
    • The Korean Journal of Applied Statistics
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    • v.13 no.2
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    • pp.237-245
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    • 2000
  • In this paper we consider a logistic regression model based on two-stage case-control sampling and study the Weighted Exogeneous Sampling Maximum Likelihood(WESML) method to get an asymptotically normal estimates of the parameters in a logistic regression model. A numerical example is carried out to demonstrate the differences between the Conditional Maximum Likelihood(CML) estimates and the WESML estimates for two-stage case-control data.

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