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Biomedical subjects

W J Padgett

Publications and source records attributed to W J Padgett.

6 recordsLinked to original sources

Accelerated degradation models for failure based on geometric Brownian motion and gamma processes.

Based on a generalized cumulative damage approach with a stochastic process describing degradation, new accelerated life test models are presented in which both observed failures and degradation measures can be considered for parametric inference of system lifetime. Incorporating an accelerated test variable, we provide several new accelerated degradation models for failure based on the geometric Brownian motion or gamma process. It is shown that in most cases, our models for failure can be approximated closely by accelerated test versions of Birnbaum-Saunders and inverse Gaussian distributions. Estimation of model parameters and a model selection procedure are discussed, and two illustrative examples using real data for carbon-film resistors and fatigue crack size are presented.

Equipment Failure Analysis↗

Inference from accelerated degradation and failure data based on Gaussian process models.

An important problem in reliability and survival analysis is that of modeling degradation together with any observed failures in a life test. Here, based on a continuous cumulative damage approach with a Gaussian process describing degradation, a general accelerated test model is presented in which failure times and degradation measures can be combined for inference about system lifetime. Some specific models when the drift of the Gaussian process depends on the acceleration variable are discussed in detail. Illustrative examples using simulated data as well as degradation data observed in carbon-film resistors are presented.

Carbon↗

Inference for reliability and stress-strength for a scaled Burr type X distribution.

Inference for R = P(Y < X) is considered when X and Y are independently distributed as scaled Burr type X random variables. Under this model, exact inference procedures for R cannot be found. Hence, based on the expected Fisher information matrix which is derived here, asymptotic inference procedures for R and other general functions of the parameters are developed. A bootstrap method to estimate variance for the maximum likelihood estimators is also discussed. To illustrate these techniques, an example using carbon fiber strength data is given. Simulations to assess the effectiveness of these techniques, as well as other concerns, are presented.

Carbon↗

Accelerated test models for system strength based on Birnbaum-Saunders distributions.

Recent research in cumulative damage models for strengths of systems has yielded various statistical distributions that incorporate a system size variable and follow a generalized Birnbaum-Saunders form. These models can be unified as a three-parameter Birnbaum-Saunders-type family of distributions, where the third parameter arises from the size variable through the cumulative damage approach. In this paper, the generalized three-parameter Birnbaum-Saunders distribution is characterized, and examples of cumulative damage models for system strength that fit this form are given. Also, estimation and asymptotic theory are developed for the generalized distribution, and illustrations are presented for experimental strength data for carbon composite materials.

Carbon Compounds, Inorganic↗

Families of smooth confidence bands for the survival function under the general random censorship model.

Randomly right censored data often arise in industrial life testing and clinical trials. Several authors have proposed asymptotic confidence bands for the survival function when data are randomly censored on the right. All of these bands are based on the empirical estimator of the survival function. In this paper, families of asymptotic (1-alpha) 100% level confidence bands are developed from the smoothed estimate of the survival function under the general random censorship model. The new bands are compared to empirical bands, and it is shown that for small sample sizes, the smooth bands have a higher coverage probability than the empirical counterparts.

Clinical Trials as Topic↗