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The Validity of Polynomial Regression in the Random Regression Model
Review of Educational Research, 1978Sockloff (1976), in reviewing the appropriateness of fixed and random models in regression analysis, has concluded "that the analysis of nonlinearity via polynomial and product regression should be limited to experimental studies" (p. 288), and that, "[r]egarding the analysis of nonlinearity in observational data under the Random Model, the Random ...
Elliot M. Cramer, Mark I. Appelbaum
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Problems in regression modeling of randomized clinical trials
Int. Journal of Clinical Pharmacology and Therapeutics, 2005Data modeling can be applied to improving the precision of clinical studies and multiple regression modeling is increasingly used for this purpose.To assess the uncertainties and risks of misinterpretations commonly encountered in regression analyses and rarely communicated in research papers.Regression analyses add uncertainties to the data in the ...
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Properties of random regression models using linear splines
Journal of Animal Breeding and Genetics, 2006SummaryProperties of random regression models using linear splines (RRMS) were evaluated with respect to scale of parameters, numerical properties, changes in variances and strategies to select the number and positions of knots. Parameters in RRMS are similar to those in multiple trait models with traits corresponding to points at knots. RRMS have good
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Random regression models: a longitudinal perspective
Journal of Animal Breeding and Genetics, 2008L R, Schaeffer, J, Jamrozik
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Cusp estimation in random design regression models
Statistics & Decisions, 2009Abstract We consider the parametric estimation for the random design nonlinear regression model whose regression function has an unknown cusp location. The Fisher information of this location parameter is unbounded, that is caused by the non-differentiability of the likelihood function, so this is a non-regular estimation problem.
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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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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
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