• Title/Summary/Keyword: Mixed-effect model

Search Result 568, Processing Time 0.021 seconds

Semiparametric and Nonparametric Mixed Effects Models for Small Area Estimation (비모수와 준모수 혼합모형을 이용한 소지역 추정)

  • Jeong, Seok-Oh;Shin, Key-Il
    • The Korean Journal of Applied Statistics
    • /
    • v.26 no.1
    • /
    • pp.71-79
    • /
    • 2013
  • Semiparametric and nonparametric small area estimations have been studied to overcome a large variance due to a small sample size allocated in a small area. In this study, we investigate semiparametric and nonparametric mixed effect small area estimators using penalized spline and kernel smoothing methods respectively and compare their performances using labor statistics.

Empirical Bayes Estimate for Mixed Model with Time Effect

  • Kim, Yong-Chul
    • Communications for Statistical Applications and Methods
    • /
    • v.9 no.2
    • /
    • pp.515-520
    • /
    • 2002
  • In general, we use the hierarchical Poisson-gamma model for the Poisson data in generalized linear model. Time effect will be emphasized for the analysis of the observed data to be collected annually for the time period. An extended model with time effect for estimating the effect is proposed. In particularly, we discuss the Quasi likelihood function which is used to numerical approximation for the likelihood function of the parameter.

Effect of kurtosis on the Flow Factors Using Average Flow Model

  • Cho, Yong-Joo;Kim, Tae-Wan;Koo, Young-Pil
    • KSTLE International Journal
    • /
    • v.3 no.1
    • /
    • pp.7-11
    • /
    • 2002
  • The roughness effects are very important due to the presence of interacting asperities in mixed lubrication regime. An average Reynolds equation using flow factors is useful to determine the effects of surface roughness on mixed lubrication. In this study, the effect of kurtosis on flow factors is investigated using random rough surfaces generated numerically, The results show that flow factors are very sensitive to h/$\sigma$ according to the value of kurtosis in the partial lubrication regime.

Quantifying the Bullwhip Effect in a Supply Chain Considering Seasonal Demand (공급사슬에서 계절적 수요를 고려한 채찍효과 측도의 개발)

  • Cho, Dong-Won;Lee, Young-Hae
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.35 no.3
    • /
    • pp.203-212
    • /
    • 2009
  • The bullwhip effect refers to the phenomenon where demand variability is amplified when one moves upward a supply chain. In this paper, we exactly quantify the bullwhip effect for cases of seasonal demand processes in a two-echelon supply chain with a single retailer and a single supplier. In most of the previous research, some measures of performance for the bullwhip effect are developed for cases of non-seasonal demand processes. The retailer performs demand forecast with a multiplicative seasonal mixed model by using the minimum mean square error forecasting technique and employs a base stock policy. With the developed bullwhip effect measure, we investigate the impact of seasonal factor on the bullwhip effect. Then, we prove that seasonal factor plays an important role on the occurrence of the bullwhip effect.

A case study on the random coefficient model for diet experimental data (변량계수모형의 식이요법 실험자료에 관한 사례연구)

  • Jo, Jin-Nam;Baik, Jai-Wook
    • Journal of the Korean Data and Information Science Society
    • /
    • v.20 no.5
    • /
    • pp.787-796
    • /
    • 2009
  • A random coefficient model is applied when times of the repeated measurements are not fixed in experiments with respect to the subjects. The procedures of the inference of a random coefficient model are same as those of a mixed model. Diet experimental data was used for applying the random coefficient model. Various random coefficient models are investigated for the experimental data, and are compared each other. Finally, optimal random coefficient model would be selected. It resulted from the analysis that for the fixed effect factor, the baseline, treatment, height, and time effect were very significant. The treatment effect of the diet foods and exercises were more effective in losing weight than the effect of the diet foods only. The fixed cubic time effect was very significant. The variance components corresponding to the subject effect, linear time effect, quadratic time effect, and cubic time effect of the random coefficients are all positive. When quartic time effect was added as random coefficients the model did not converge. Thus random coefficients up to the cubic terms was considered as the optimal model.

  • PDF

Optimal Road Maintenance Section Selection Using Mixed Integer Programming (혼합정수계획법을 활용한 도로포장 보수구간 선정 최적화 연구)

  • Cho, Geonyoung;Lim, Heejong
    • International Journal of Highway Engineering
    • /
    • v.19 no.3
    • /
    • pp.65-70
    • /
    • 2017
  • PURPOSES : Pavement Management System contains the data that describe the condition of the road. Under limited budget, the data can be utilized for efficient plans. The objective of this research is to develop a mixed integer program model that maximizes remaining durable years (or Lane-Kilometer-Years) in road maintenance planning. METHODS : An optimization model based on a mixed integer program is developed. The model selects a cluster of sectors that are adjacent to each other according to the road condition. The model also considers constraints required by the Seoul Metropolitan Facilities Management Corporation. They select two lanes at most not to block the traffic and limit the number of sectors for one-time construction to finish the work in given time. We incorporate variable cost constraints. As the model selects more sectors, the unit cost of the construction becomes smaller. The optimal choice of the number of sectors is implemented using piecewise linear constraints. RESULTS : Data (SPI) collected from Pavement Management System managed by Seoul Metropolitan City are fed into the model. Based on the data and the model, the optimal maintenance plans are established. Some of the optimal plans cannot be generated directly in existing heuristic approach or by human intuition. CONCLUSIONS:The mathematical model using actual data generates the optimal maintenance plans.

Experimental study on treatment of waste slurry by vacuum preloading with different conditioning agents

  • Wu, Yajun;Jiang, Haibo;Lu, Yitian;Sun, Dean
    • Geomechanics and Engineering
    • /
    • v.17 no.6
    • /
    • pp.543-551
    • /
    • 2019
  • In China, serious environmental problems are induced by the extremely soft construction waste slurries in many urban areas, and there is no appropriate method to treat it presently. In this paper, four model tests were conducted to investigate the efficiency of waste slurry treatment by combining three conditioning agents which can change characteristics of the slurries with a traditional vacuum preloading method. The tests of size analysis of particle aggregate were conducted to investigate the influence of different conditioning agents on the size distributions of particle aggregate. During the model test, the discharged water volumes were monitored. The pore-size distribution and void ratio of the waste slurries after the vacuum preloading were measured by mercury intrusion porosimetry (MIP). It is found that 1) During the natural precipitation, volume of water out of the organic agent is higher than that of the mixed agent, but it is smaller than that of the mixed agent in the vacuum preloading stage; 2) the mixed agent has a higher total volume of water out than the organic agent and the inorganic agent after test, while the organic agent and the inorganic agent have little difference with respect to the drainage effect. The results demonstrate that the combination of mixed conditioning agent and vacuum preloading for the solid-liquid separation in waste slurry has a satisfactory effect and can be applied in engineering practice.

Nonnegative variance component estimation for mixed-effects models

  • Choi, Jaesung
    • Communications for Statistical Applications and Methods
    • /
    • v.27 no.5
    • /
    • pp.523-533
    • /
    • 2020
  • This paper suggests three available methods for finding nonnegative estimates of variance components of the random effects in mixed models. The three proposed methods based on the concepts of projections are called projection method I, II, and III. Each method derives sums of squares uniquely based on its own method of projections. All the sums of squares in quadratic forms are calculated as the squared lengths of projections of an observation vector; therefore, there is discussion on the decomposition of the observation vector into the sum of orthogonal projections for establishing a projection model. The projection model in matrix form is constructed by ascertaining the orthogonal projections defined on vector subspaces. Nonnegative estimates are then obtained by the projection model where all the coefficient matrices of the effects in the model are orthogonal to each other. Each method provides its own system of linear equations in a different way for the estimation of variance components; however, the estimates are given as the same regardless of the methods, whichever is used. Hartley's synthesis is used as a method for finding the coefficients of variance components.

Modelling Online Word-of-Mouth Effect on Korean Box-Office Sales Based on Kernel Regression Model

  • Park, Si-Yun;Kim, Jin-Gyo
    • Journal of the Korean Data and Information Science Society
    • /
    • v.18 no.4
    • /
    • pp.995-1004
    • /
    • 2007
  • In this paper, we analyse online word-of-mouth and Korean box-office sales data based on kernel regression method. To do this, we consider the regression model with mixed-data and apply the least square cross-validation method proposed by Li and Racine (2004) to the model. We found the box-office sales can be explained by volume of online word-of-mouth and the characteristics of the movies.

  • PDF

A Study of HME Model in Time-Course Microarray Data

  • Myoung, Sung-Min;Kim, Dong-Geon;Jo, Jin-Nam
    • The Korean Journal of Applied Statistics
    • /
    • v.25 no.3
    • /
    • pp.415-422
    • /
    • 2012
  • For statistical microarray data analysis, clustering analysis is a useful exploratory technique and offers the promise of simultaneously studying the variation of many genes. However, most of the proposed clustering methods are not rigorously solved for a time-course microarray data cluster and for a fitting time covariate; therefore, a statistical method is needed to form a cluster and represent a linear trend of each cluster for each gene. In this research, we developed a modified hierarchical mixture of an experts model to suggest clustering data and characterize each cluster using a linear mixed effect model. The feasibility of the proposed method is illustrated by an application to the human fibroblast data suggested by Iyer et al. (1999).