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Maximin -optimal designs for binary longitudinal responses
Computational Statistics & Data Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tekle, F.B., Tan, F.E.S., Berger, M.P.F.
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1998
This chapter is concerned with estimating the binary-response model $$ Y = \left\{{\begin{array}{*{20}{c}} {1\;if\;Y* > 0\;}\\ {0\;otherwise} \end{array}} \right. $$ (3.1a) where $$ {Y^*} = X\beta+U $$ (3.1b) Y is the observed dependent variable, X is a 1 × k vector of observed explanatory variables, β is a k × 1 vector of constant ...
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This chapter is concerned with estimating the binary-response model $$ Y = \left\{{\begin{array}{*{20}{c}} {1\;if\;Y* > 0\;}\\ {0\;otherwise} \end{array}} \right. $$ (3.1a) where $$ {Y^*} = X\beta+U $$ (3.1b) Y is the observed dependent variable, X is a 1 × k vector of observed explanatory variables, β is a k × 1 vector of constant ...
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Realized heritability of binary behavioral responses
Behavior Genetics, 1985The distribution of a sample of binary responses, such asSCORE = the number of positive responses ofm trials, can be modeled by the betabinomial compound density. The heritability ofSCORE is estimated following the method detailed by W. G. Hill [(1972). Biometrics27:293–311], but the formula for the standard error is changed.
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Feedback Control for Binary Response
2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2017Defect rate control is crucial in industries. When binary response is considered, the defect rate is the average of these binary responses. In this study, with logistic regression model and sparsity assumption, the feedback control problem is expressed as an optimization problem which solves a hinge loss with an L1 penalty. Here the hinge loss function
Ping-Yang Chen +4 more
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Reliability of binary analytical responses
TrAC Trends in Analytical Chemistry, 2005Abstract Binary response is clearly qualitative analytical information, which is become more and more important, as it is recognized as rapid information to solve solutions to a wide variety of problems. The metrology and the corresponding quality-assurance issues associated with this type of information are just being developed.
Angel Ríos, Helena Téllez
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Growth Curve Models of Repeated Binary Response
Biometrics, 1988Experimental designs that include repeated measures of binary response variables over time and under different conditions are common in biology. In such settings, it is often desirable to characterize the response pattern over time. When response variables are continuous, this characterization can be made in terms of a growth model such as the Potthoff-
E J, Stanek, S R, Diehl
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Crossover Studies with Binary Responses
2011The two-period crossover trial has the evident advantage that by the use of within-patients comparisons, the usually larger between-patient variability is not used as a measuring stick to compare treatments. However, a prerequisite is that the order of the treatments does not substantially influence the outcome of the treatment.
Ton J. Cleophas, Aeilko H. Zwinderman
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Hierarchical models for multicentre binary response studies
Statistics in Medicine, 1990AbstractA three‐stage hierarchical model is proposed for two treatment, binary response studies conducted in a number of centres. The approach adopted is Bayesian. Marginal densities for second stage parameters are shown to provide useful summaries both of comparative efficacy and of the heterogeneity of treatment effects across centres.
A M, Skene, J C, Wakefield
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Binary and Graded Responses in Gene Networks
Science Signaling, 2002Although gene expression can be regulated in a graded or a binary fashion, the majority of eukaryotic genes are either fully activated or not expressed at all in individual cells. This binary response might be an inherent property of many eukaryotic promoters. Analysis of transcription under the control of yeast GAL1
Louis, Matthieu, Becskei, Attila
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Models for Binary Response Variables
1995The test procedures in the linear regression model are based on the normal distribution of the error variable ∊ and thus on a normal distribution of the endogenous variable Y. However, in many fields of application this assumption may not be true. The response variable Y may be defined as a binary variable, or more generally, as a categorical variable.
Calyampudi Radhakrishna Rao +1 more
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