Stacked survival models for residual lifetime data

When modelling the survival distribution of a disease for which the symptomatic progression of the associated condition is insidious, it is not always clear how to measure the failure/censoring times from some true date of disease onset. In a prevalent cohort study with follow-up, one approach for removing any potential influence from the uncertainty in the measurement of the true onset dates is through the utilization of only the residual lifetimes. As the residual lifetimes are measured from a well-defined screening date (prevalence day) to failure/censoring, these observed time durations are essentially error free. Using residual lifetime data, the nonparametric maximum likelihood estimator (NPMLE) may be used to estimate the underlying survival function. However, the resulting estimator can yield exceptionally wide confidence intervals. Alternatively, while parametric maximum likelihood estimation can yield narrower confidence intervals, it may not be robust to model misspecification. Using only right-censored residual lifetime data, we propose a stacking procedure to overcome the non-robustness of model misspecification; our proposed estimator comprises a linear combination of individual nonparametric/parametric survival function estimators, with optimal stacking weights obtained by minimizing a Brier Score loss function.

Alpha Power Extended Inverse Weibull Poisson Distribution: Properties, Inference, and Applications to lifetime data

In this paper, a new four-parameter extended inverse Weibull distribution called Alpha power Extended Inverse Weibull Poisson distribution is introduced using the alpha power Poisson generator. This method adds two shape parameters to a baseline distribution thereby increasing its flexibility and applicability in modeling lifetime data. We study the structural properties of the new distribution such as the mean, variance, quantile function, median, ordinary and incomplete moments, reliability analysis, Lorenz and Bonferroni curves, Renyi entropy, mean waiting time, mean residual life, and order statistics. We use the method of maximum likelihood technique for estimating the model parameters of Alpha power extended inverse Weibull distribution and the corresponding confidence intervals are obtained. The simulation method is carried out to evaluate the performance of the maximum likelihood estimate in terms of their Absolute Bias and Mean Square Error using simulated data. Two lifetime data sets are presented to demonstrate the applicability of the new model and it is found that the new model has superior modeling power when compare to Inverse Weibull distribution, Alpha Power Poisson inverse exponential distribution, Alpha Power Extended Inverse Weibull distribution, and Alpha Power Extended Inverse Exponential distribution.

The McDonald Lindley-Poisson Distribution

We propose the McDonald Lindley-Poisson distribution and derive some of its mathematical properties including explicit expressions for moments, generating and quantile functions, mean deviations, order statistics and their moments. Its model parameters are estimated by maximum likelihood. A simulation study investigates the performance of the estimates. The new distribution represents a more flexible model for lifetime data analysis than other existing models as proved empirically by means of two real data sets.

A Practical Guide to Family Studies with Lifetime Data

Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A Notable Bounded Probability Distribution for Environmental and Lifetime Data

In this article, we introduce a notable bounded distribution based on a modification of the epsilon function which creates an upper bound on the domain of the distribution. Further, a key feature of the distribution links the readers with the asymptotic connections with the famous Lindley distribution, which is a weighted variant of the exponential distribution and also a mixture of exponential and gamma distributions. In some ways, the proposed distribution provides a flexible solution to the modeling of bounded characteristics that can be almost well-fitted by the Lindley distribution if the domain is restricted. Moreover, we have also explored its application, particularly with reference to lifetime and environmental points of view, and found that the proposed model exhibits a better fit among the competing models. Further, from the annual rainfall analysis, the proposed model exhibits a realistic return period of the rainfall.

The Topp-Leone Weibull Distribution: Its Properties and Application

This paper presents a new generalization of the Topp-Leone distribution called the Topp-Leone Weibull Distribution (TLWD). Some of the mathematical properties of the proposed distribution are derived, and the maximum likelihood estimation method is adopted in estimating the parameters of the proposed distribution. An application of the proposed distribution alongside with some well-known distributions belonging to the Topp-Leone generated family of distributions, to a real lifetime data set reveals that the proposed distribution exhibits more flexibility in modeling lifetime data based on some comparison criteria such as maximized log-likelihood, Akaike Information Criterion [AIC=2k-2 log⁡(L) ], Kolmogorov-Smirnov test statistic (K-S) and Anderson Darling test statistic (A*) and Crammer-Von Mises test statistic (W*).