A note on interval‐censored lifetime data and the constant‐sum condition of oiler,gómez & calle (2004)

Oiler, Gomez & Calle (2004) give a constant sum condition for processes that generate interval‐censored lifetime data. They show that in models satisfying this condition, it is possible to estimate non‐parametrically the lifetime distribution based on a well‐known simplified likelihood. The author shows that this constant‐sum condition is equivalent to the existence of an observation process that is independent of lifetimes and which gives the same probability distribution for the observed data as the underlying true process.     

An evaluation of the rural development commission's redundant building grant

The Rural Development Grant operates the Redundant Building Grant which is designed to bring redundant buildings in certain areas into commercial activity. This paper outlines the results of a recent evaluation of the Grant largely based on contacts with more than 400 applicants in nine counties. This survey is used to explore five aspects of rural policy: property development; business performance; job creation; integrated rural development; and aspects of land use planning.     

Models and estimation for systems with recurrent events and usage processes

This paper considers settings where populations of units may experience recurrent events, termed failures for convenience, and where the units are subject to varying levels of usage. We provide joint models for the recurrent events and usage processes, which facilitate analysis of their relationship as well as prediction of failures. Data on usage are often incomplete and we show how to implement maximum likelihood estimation in such cases. Random effects models with linear usage processes and gamma usage processes are considered in some detail. Data on automobile warranty claims are used to illustrate the proposed models and estimation methodology.     

Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure

The gamma process is a natural model for degradation processes in which deterioration is supposed to take place gradually over time in a sequence of tiny increments. When units or individuals are observed over time it is often apparent that they degrade at different rates, even though no differences in treatment or environment are present. Thus, in applying gamma-process models to such data, it is necessary to allow for such unexplained differences. In the present paper this is accomplished by constructing a tractable gamma-process model incorporating a random effect. The model is fitted to some data on crack growth and corresponding goodness-of-fit tests are carried out. Prediction calculations for failure times defined in terms of degradation level passages are developed and illustrated.     

Semiparametric Inference Methods for General Time Scale Models

In this paper we consider semiparametric inference methods for the time scale parameters in general time scale models (Oakes, 1995, Duchesne and Lawless, 2000). We use the results of Robins and Tsiatis (1992) and Lin and Ying (1995) to derive a rank-based estimator that is more efficient and robust than the traditional minimum coefficient of variation (min CV) estimator of Kordonsky and Gerstbakh (1993) for many underlying models. Moreover, our estimator can readily handle censored samples, which is not the case with the min CV method.     

Pseudolikelihood estimation in a class of problems with response-related missing covariates

Many practical situations involve a response variable Y and covariates X , where data on (Y, X ) are incomplete for some portion of a sample of individuals. We consider two general types of pseudolikelihood estimation for problems in which missingness may be response-related. These are typically simpler to implement than ordinary maximum likelihood, which in this context is semiparametric. Asymptotics for the pseudolikelihood methods are presented, and simulations conducted to investigate the methods for an important class of problems involving lifetime data. Our results indicate that for these problems the two methods are effective and comparable with respect to efficiency.     

Enhancing the chances of successful OR/MS implementation: the role of the advocate

This paper extends understanding of the role of the advocate in the implementation of Operations Research/Management Science models in governmental agencies. The first part of this paper presents and analyzes the data resulting from interviews with 39 criminal justice agencies regarding the model implementation process. The interview data indicated that the advocate performed important problem-solving and mediation functions needed to introduce the technology. The second part of this paper provides a case study describing an advocate's successful introduction of a Patrol Car Allocation Model (PCAM) in the Los Angeles County Sheriff's Department. To assess the overall effectiveness of their implementation strategy, the project team conducted a questionnaire attitudinal survey of lieutenants, sergeants and officers in two of the pilot stations prior to PCAM implementation and after staffing changes. Overall, the attitude of the respondents was more favorable at the time of the second survey. The analysis of the interviews and the PCAM case study suggests a simple normative model for enhancing the chances of successful implementation of Operations Research/Management Science models.     

A simulation study of ridge and other regression estimators

We consider a number of estimators of regression coefficients, all of generalized ridge, or 'shrinkage' type. Results of a simulation study indicate that with respect to two commonly used mean square error criteria, two ordinary ridge estimators, one proposed by Hoerl, Kennard and Baldwin, and the other introduced here, perform substantially better than both least squares and the other estimators discussed here     

Failure Inference From a Marker Process Based on a Bivariate Wiener Model

Many models have been proposed that relate failure times and stochastic time-varying covariates. In some of these models, failure occurs when a particular observable marker crosses a threshold level. We are interested in the more difficult, and often more realistic, situation where failure is not related deterministically to an observable marker. In this case, joint models for marker evolution and failure tend to lead to complicated calculations for characteristics such as the marginal distribution of failure time or the joint distribution of failure time and marker value at failure. This paper presents a model based on a bivariate Wiener process in which one component represents the marker and the second, which is latent (unobservable), determines the failure time. In particular, failure occurs when the latent component crosses a threshold level. The model yields reasonably simple expressions for the characteristics mentioned above and is easy to fit to commonly occurring data that involve the marker value at the censoring time for surviving cases and the marker value and failure time for failing cases. Parametric and predictive inference are discussed, as well as model checking. An extension of the model permits the construction of a composite marker from several candidate markers that may be available. The methodology is demonstrated by a simulated example and a case application.     

Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates

Two- or multi-phase study designs are often used in settings involving failure times. In most studies, whether or not certain covariates are measured on an individual depends on their failure time and status. For example, when failures are rare, case–cohort or case–control designs are used to increase the number of failures relative to a random sample of the same size. Another scenario is where certain covariates are expensive to measure, so they are obtained only for selected individuals in a cohort. This paper considers such situations and focuses on cases where we wish to test hypotheses of no association between failure time and expensive covariates. Efficient score tests based on maximum likelihood are developed and shown to have a simple form for a wide class of models and sampling designs. Some numerical comparisons of study designs are presented.