Recurrent Events Data Analysis for Product Repairs, Disease by Wayne B. Nelson

By Wayne B. Nelson

Survival info encompass a unmarried occasion for every inhabitants unit, specifically, finish of lifestyles, that's modeled with a existence distribution. by contrast, many functions contain repeated-events information, the place a unit might gather any variety of occasions over the years. Examples comprise the quantity and price of maintenance of goods, the quantity and therapy charges of recurrent disorder episodes in sufferers, and the variety of childbirths to statisticians. This utilized ebook presents practitioners with uncomplicated nonparametric tools for such information, quite the plot of the estimate of the inhabitants suggest cumulative functionality (MCF), which yields lots of the info sought. Recurrent occasions information research for Product maintenance, ailment Recurrences, and different functions is the 1st booklet to give an easy, unified idea that incorporates facts on expenses or different 'values' of discrete occasions, not only the variety of occasions.

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Such histories may not be randomly censored. The model and theory in this book must be modified to handle such termination. Wang, Qin, and Chiang (2001) provide a model and analysis for such nonrandom censoring (also called informative censoring). 3 Population Model and Its MCF Purpose. This section presents the nonparametric population model, population distributions, and population MCF. Model. The nonparametric model for a population of units is the population of the cumulative history functions of all units.

In the transmission data, there are no ties. 1. (b) Number at risk. For each sample age, write in column 2 the number r of remaining units ("at risk" of recurrence) at that age as follows. For the earliest age, write r = N — 1 if it is censored. Otherwise, write r = N if it is a recurrence. Then proceed down column 2, writing the preceding number r for each recurrence age or writing the preceding number minus 1 for each censoring age. That is, the remaining observed number r decreases by 1 at each censoring age.

Discrete distribution. 4 shows five population staircase history functions for the cumulative number of events. 3 are easier to see. 5, a large population of such functions at age t has a discrete distribution of the cumulative numbers of events, which has integer values 0,1, 2, 3, — These values are the heights of all staircase functions at age t. At age t, a fraction of the population has accumulated zero recurrences, another fraction of the population has accumulated one recurrence, another fraction has accumulated two recurrences, etc.

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