Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

Survival analysis:techniques for censored and truncated data , Survival analysis:techniques for censored and truncated data , کتابخانه مرکزی دانشگاه علوم پزشکی ایران

Survival Analysis: Techniques for Censored and Truncated Data

Survival Analysis: Techniques for Censored and Truncated Data by

Objective To describe the relapse curve and rate of long-term prolonged abstinence among smokers who try to quit without treatment.

Method  Systematic literature review.

Data sources  Cochrane Reviews, Dissertation Abstracts, Excerpt Medica, Medline, Psych Abstracts and US Center for Disease Control databases plus bibliographies of articles and requests of scientists.

Study selection  Prospective studies of self-quitters or studies that included a no-treatment control group.

Data extraction  Two reviewers independently extracted data in a non-blind manner.

Data synthesis  The number of studies was too small and the data too heterogeneous for meta-analysis or other statistical techniques.

Results  There is a paucity of studies reporting relapse curves of self-quitters. The existing eight relapse curves from two studies of self-quitters and five no-treatment control groups indicate most relapse occurs in the first 8 days. These relapse curves were heterogeneous even when the final outcome was made similar. In terms of prolonged abstinence rates, a prior summary of 10 self-quitting studies, two other studies of self-quitters and three no-treatment control groups indicate 3–5% of self-quitters achieve prolonged abstinence for 6–12 month after a given quit attempt.

Conclusions  More reports of relapse curves of self-quitters are needed. Smoking cessation interventions should focus on the first week of abstinence. Interventions that produce abstinence rates of 5–10% may be effective. Cessation studies should report relapse curves.

Shape of the relapse curve and long-term abstinence among untreated smokers

Features of Financial Returns Modelling Price Volatility Forecasting Standard Deviations The Accuracy of Autocorrelation Estimates Testing the Random Walk Hypothesis Forecasting Trends in Prices Evidence Against the Efficiency of Futures Markets Valuing Options Appendix: A Computer Program for Modelling Financial Time Series.

Modelling Financial Time Series

Formulating Tests for the Presence of Immunes and Sufficient Follow-up. Properties of the Kaplan-Meier Estimator. Nonparametric Estimation and Testing. Parametric Models for Single Samples. The Use of Concomitant Information. Large Sample Properties of Parametric Models: Single Samples. Large-Sample Properties of Parametric Models with Covariates. Further Topics. References. Statistical Tables. Index.

Survival analysis with long-term survivors

Survival Analysis with Long-Term Survivors

The estimation of loss reserves for incurred but not reported (IBNR) claims presents an important task for insurance companies to predict their liabilities. Recently, individual claim loss models have attracted a great deal of interest in the actuarial literature, which overcome some shortcomings of aggregated claim loss models. The dependence of the event times with the delays is a crucial issue for estimating the claim loss reserving. In this article, we propose to use semi-competing risks copula and semi-survival copula models to fit the dependence structure of the event times with delays in the individual claim loss model. A nonstandard two-step procedure is applied to our setting in which the associate parameter and one margin are estimated based on an ad hoc estimator of the other margin. The asymptotic properties of the estimators are established as well. A simulation study is carried out to evaluate the performance of the proposed methods.

Applying copula models to individual claim loss reserving methods

Stochastic scheduling to minimize expected maximum lateness

The estimation of loss reserves for incurred but not reported (IBNR) claims presents an important task for insurance companies to predict their liabilities. Conventional methods, such as ladder or separation methods based on aggregated or grouped claims of the so-called “run-off triangle”, have been illustrated to have some drawbacks. Recently, individual claim loss models have attracted a great deal of interest in actuarial literature, which can overcome the shortcomings of aggregated claim loss models. In this paper, we propose an alternative individual claim loss model, which has a semiparametric structure and can be used to fit flexibly the claim loss reserving. Local likelihood is employed to estimate the parametric and nonparametric components of the model, and their asymptotic properties are discussed. Then the prediction of the IBNR claim loss reserving is investigated. A simulation study is carried out to evaluate the performance of the proposed methods.

Xian Zhou

Papers

Survival analysis with long-term survivors

Survival Analysis with Long-Term Survivors

Applying copula models to individual claim loss reserving methods

Stochastic scheduling to minimize expected maximum lateness

Semiparametric model for prediction of individual claim loss reserving