• Journal of Applied Reliability
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• v.2 no.2
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• pp.63-83
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• 2002

Degradation data can provide more reliability information than traditional failure-time data, especially products with few or no failures. This paper is concerned with a method of estimating lifetime distribution from field data with supplementary information on degradation data and covariates. When a distribution of degradation rate obtained by follow-up study for a portion of products that survive after-warranty follows a reciprocal-Weibull or lognormal distribution. A time-to-failure distribution of the product follows Weibull or lognormal distribution, respectively. A method of estimating lifetime parameters for this kind of data and their asymptotic properties are studied. Effects of after-warranty report probability, follow-up rate, and proportion of degradation data on pseudo maximum likelihood estimators of these parameters are investigated.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.659-664
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• 2008

Missing continuous covariates are pervasive in the use of generalized linear models for medical data. Multiple imputation is the most common and easy-to-do method of dealing with missing covariate data. However, there are always serious warnings in using this method. There should be concern to make imputed values more proper. In this paper, proper imputation from posterior predictive distribution is developed for implementing with arbitrary priors. We use empirical distribution of the posterior for approximating the posterior predictive distribution, to sample from it. This method is preferable in comparison with a presented imputation method of us which uses a full model to impute missing values using available software. The proposed methods are implemented on glucocorticoid data.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1017-1026
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• 2005

We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.433-444
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• 1996

In contrast to the multiplicative risk model, the additive risk model specifies that the hazard function with covariates is the sum of, rather than product of, the baseline hazard function and the regression function of covariates. We, in this paper, propose a method for checking the adequacy of the additive risk model based on partial-sum of matingale residuals. Under the assumed model, the asymptotic properties of the proposed test statistic and approximation method to find the critical values of the limiting distribution are studied. Several real examples are illustrated.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.295-302
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• 2000

We study the efficient method to estimate the parameters for the Weibull model with covariates which occupies an important position in survival analysis. A treatment period may be divided by the stages of treatments under the different treatment arams. The piecewise method is considered to obtain the estimators of the parameters by maximum likelihood method. We explore the real data to show that the piecewise is more efficient than the nonpiecewise to estimate the parameters.

• International Journal of Reliability and Applications
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• v.4 no.2
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• pp.79-95
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• 2003

Manufacturers collect and analyze field reliability data to enhance the quality and reliability of their products and to improve customer satisfaction. To reduce the data collecting and maintenance costs, the amount of data maintained for evaluating product quality and reliability should be minimized. With this in mind, some industrial companies assemble warranty databases by gathering data from different sources for a particular time period. This “marginal count failure data” does not provide (i) the number of failures by when the product entered service, (ii) the number of failures by product age, or (iii) information about the effects of the operating season or environment. This article describes a method for estimating age-based claim rates from marginal count failure data. It uses covariates to identify variations in claims relative to variables such as manufacturing characteristics, time of manufacture, operating season or environment. A Poisson model is presented, and the method is illustrated using warranty claims data for two electrical products.

• International Journal of Reliability and Applications
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• v.3 no.4
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• pp.185-198
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• 2002

In the traditional life testing model, it is assumed that a certain number of identical items are tested under identical condition. This is due to statistical rather than practical considerations. The proportional hazards model can be used to develop a realistic approach to determine the performance of an item. That is also capable of modeling the failure rates of accelerated life testing when the covariates are applied stresses. The proportional hazards model is typically applied for a group of items to assess the importance of factors that may influence the reliability of an item. In this paper we considered the interarrival times of an item rather than the time to first failure for grouped items and provided the availability estimation for the determination of maintenance policy and overhaul time. In order to demonstrate the proposed approach, an example is presented.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.1-12
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• 2004

In this paper, we consider choosing the appropriate covariance structure for analyzing repeated measures data with three repeat factors from a study of blood pressure data, which is collected from the local residents of Yangpyeong, Gyeonggi-do (2001) and fitted linear mixed models to find the significant covariates on outcome variable(Blood Pressure)

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1263-1272
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• 2014

Longitudinal categorical data commonly occur from medical, health, and social sciences. In these data, the correlation of repeated outcomes is taken into account to explain the effects of covariates exactly. In this paper, we introduce marginalized random effects models that are used for the estimation of the population-averaged effects of covariates. We also review how these models have been developed. Real data analysis is presented using the marginalized random effects.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.4
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• pp.510-516
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• 1999

This paper proposes methods of estimating lifetime distribution from incomplete field data under parametric regression models. Failure-record data-failure times and covariates-reported to the manufacturer can be seriously incomplete for satisfactory inference since only reported failures are recorded. This paper assumes that within-warranty data are reported with probability $P_1$ ($\leq1$) and after-warranty data are reported with Methods of obtaining pseudo and after-warranty data are reported with $P_2$ (< $P_1$). Methods of obtaining pseudo maximum likelihood estimators(PMLEs) are outlined, their asymptotic properties are studied, and specific formulas for Weibull distribution are obtained. Simulation studies are perfumed to investigate the effects of follow-up percentage on the PMLEs.