Non-parametric Bootstrap Confidence Limits for Age-Dependent Failure Tendency Using Incomplete Data

2014 ◽  
pp. 277-286 ◽  
Author(s):  
Per-Erik Hagmark ◽  
Jouko Laitinen
Keyword(s):  
Incomplete Data ◽  
Parametric Bootstrap ◽  
Confidence Limits ◽  
Age Dependent ◽  
Dependent Failure ◽  
Non Parametric

scholarly journals Estimating Total Energy Demand from Incomplete Data Using Non-parametric Analysis

2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Benard Mworia Warutumo ◽  
Pius Nderitu Kihara ◽  
Levi Mbugua
Keyword(s):  
Total Energy ◽  
Incomplete Data ◽  
Parametric Analysis ◽  
Energy Demand ◽  
Non Parametric

scholarly journals ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING

2018 ◽  
Vol 48 (3) ◽  
pp. 199-204 ◽  
Author(s):  
R. LI ◽  
J. ZHOU ◽  
L. WANG
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Parametric Bootstrap ◽  
Binary Logistic Regression Model ◽  
Bayesian Bootstrap ◽  
Non Parametric

In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the maximum likelihood method for parameter estimation in the binary logistic regression model.

Warranty costs: An age-dependent failure/repair model

2004 ◽  
Vol 51 (7) ◽  
pp. 959-976 ◽  
Author(s):  
Boyan Dimitrov ◽  
Stefanka Chukova ◽  
Zohel Khalil
Keyword(s):  
Age Dependent ◽  
Warranty Costs ◽  
Dependent Failure

Non-parametric bootstrap ensembles for detection of tumor lesions

2007 ◽  
Vol 28 (16) ◽  
pp. 2273-2283 ◽  
Author(s):  
Mireya Diaz ◽  
J. Sunil Rao
Keyword(s):  
Parametric Bootstrap ◽  
Non Parametric

scholarly journals Improved non-parametric statistical methods for the estimation of Michaelis-Menten kinetic parameters by the direct linear plot

1977 ◽  
Vol 161 (2) ◽  
pp. 293-302 ◽  
Author(s):  
W R Porter ◽  
W F Trager
Keyword(s):  
Statistical Methods ◽  
Data Fitting ◽  
Confidence Limits ◽  
Estimated Parameters ◽  
Lower Confidence ◽  
Proper Design ◽  
Linear Plot ◽  
Replicate Measurements ◽  
Menten Kinetic ◽  
Non Parametric

The theoretical basis for the direct linear plot [Eisenthal & Cornish-Bowden (1974) Biochem. J. 139, 715-720], a non-parametric statistical method for the analysis of data-fitting the Michaelis-Menten equation, was reinvestigated in order to accommodate additional experimental designs and to provide estimates of precision more directly comparable with those obtained by parametric statistical methods. Methods are given for calculating upper and lower confidence limits for the estimated parameters, for accommodating replicate measurements and for comparing the results of two separate experiments. Factors that influence the proper design of experiments are discussed.

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