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A regression model for multivariate random length data
Statistics in Medicine, 1999Multivariate random length data occur when we observe multiple measurements of a quantitative variable and the variable number of these measurements is also an observed outcome for each experimental unit. For example, for a patient with coronary artery disease, we may observe a number of lesions in that patient's coronary arteries, along with ...
H X, Barnhart +2 more
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Prediction in Random Coefficient Regression Models
Biometrical Journal, 1990AbstractMuch attention has been given to the problem of predicting future observations for some individual within a random coefficient regression (RCR) model, using the previous observations on that individual as well as the information from the rest of the data material.
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Drop-outs and a random regression model
Journal of Biopharmaceutical Statistics, 1997The implications of drop-outs for power of random regression model (RRM) tests of significance for differences in the rate of change produced by two treatments in a randomized parallel-groups design were investigated by Monte Carlo simulation methods. The two-stage RRM fitted a least squares linear regression equation to all of the available data for ...
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A Random-Effects Ordinal Regression Model for Multilevel Analysis
Biometrics, 1994A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data. This model is developed for both the probit and logistic response functions. The threshold concept is used, in which it is assumed that the observed ordered category is determined by the value of a latent unobservable continuous ...
Hedeker, Donald, Gibbons, Robert D.
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Efficient Inference in a Random Coefficient Regression Model
Econometrica, 1970Computes a GLS matrix weighted estimator for a panel data set. meangroup.src does a similar estimator, but uses simple weighted average rather than a matrix-weighted average. Swamy(1970), "Efficient Inference in a Random Coefficient Regression Model", Econometrica, vol 38, 311-323. (This abstract was borrowed from another version of this item.)
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Random weightingT-statistics in linear regression models
Acta Mathematica Sinica, 1995Summary: We have constructed a random weighting statistic to approximate the distribution of the Studentized least square estimator in a linear regresion model with ideal accuracy \(o(n^{- 1/2})\). Thus, we have provided a more practical distribution approximation method.
Shi, Jian, Zheng, Zhongguo
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Regression Model Based on Fuzzy Random Variables
2008In real-world regression problems, various statistical data may be linguistically imprecise or vague. Because of such co-existence of random and fuzzy information, we can not characterize the data only by random variables. Therefore, one can consider the use of fuzzy random variables as an integral component of regression problems.
Shinya Imai, Shuming Wang, Junzo Watada
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-optimal designs in random coefficient regression models
Statistics & Probability Letters, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liu, Xin +2 more
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LEARNING RANDOM MODEL TREES FOR REGRESSION
International Journal of Computers and Applications, 2011AbstractRegression is one of the most important tasks in real-world data mining applications. Among a large number of regression models, model tree is an excellent regression model. In this paper, we single out an improved model tree algorithm via introducing randomness into the process of building model trees.
Chaoqun Li, Hongwei Li
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Regression models for Boolean random sets
Journal of Applied Statistics, 2006Abstract In this paper we consider the regression problem for random sets of the Boolean-model type. Regression modeling of the Boolean random sets using some explanatory variables are classified according to the type of these variables as propagation, growth or propagation-growth models.
M. Khazaee, K. Shafie
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