Definition
Discrete time
Let be a random variable, which is defined on theGeneral case
Let be a random variable, which is defined on theAs adapted process
LetComments
Some authors explicitly exclude cases whereExamples
To illustrate some examples of random times that are stopping rules and some that are not, consider a gambler playing roulette with a typical house edge, starting with $100 and betting $1 on red in each game: *Playing exactly five games corresponds to the stopping time ''τ'' = 5, and ''is'' a stopping rule. *Playing until they either run out of money or have played 500 games ''is'' a stopping rule. *Playing until they are the maximum amount ahead they will ever be ''is not'' a stopping rule and does not provide a stopping time, as it requires information about the future as well as the present and past. *Playing until they double their money (borrowing if necessary) ''is not'' a stopping rule, as there is a positive probability that they will never double their money. *Playing until they either double their money or run out of money ''is'' a stopping rule, even though there is potentially no limit to the number of games they play, since the probability that they stop in a finite time is 1. To illustrate the more general definition of stopping time, considerLocalization
Stopping times are frequently used to generalize certain properties of stochastic processes to situations in which the required property is satisfied in only a local sense. First, if ''X'' is a process and τ is a stopping time, then ''X''''τ'' is used to denote the process ''X'' stopped at time ''τ''. :Local martingale process. A process ''X'' is a local martingaleif it is càdlàg and there exists a sequence of stopping times τ''n'' increasing to infinity, such that :
Locally integrable process. A non-negative and increasing process ''X'' is locally integrable if there exists a sequence of stopping times ''τ''''n'' increasing to infinity, such that :\operatorname \left mathbf_X^ \right \infty for each ''n''.
Types of stopping times
Stopping times, with time index set ''I'' = [0,∞), are often divided into one of several types depending on whether it is possible to predict when they are about to occur. A stopping time ''τ'' is predictable if it is equal to the limit of an increasing sequence of stopping times ''τ''''n'' satisfying ''τ''''n'' < ''τ'' whenever ''τ'' > 0. The sequence ''τ''''n'' is said to ''announce'' ''τ'', and predictable stopping times are sometimes known as ''announceable''. Examples of predictable stopping times are hitting times of continuous and Adapted process, adapted processes. If ''τ'' is the first time at which a continuous and real valued process ''X'' is equal to some value ''a'', then it is announced by the sequence ''τ''''n'', where ''τ''''n'' is the first time at which ''X'' is within a distance of 1/''n'' of ''a''. Accessible stopping times are those that can be covered by a sequence of predictable times. That is, stopping time ''τ'' is accessible if, P(''τ'' = ''τ''''n'' for some ''n'') = 1, where ''τ''''n'' are predictable times. A stopping time ''τ'' is totally inaccessible if it can never be announced by an increasing sequence of stopping times. Equivalently, P(''τ'' = ''σ'' < ∞) = 0 for every predictable time ''σ''. Examples of totally inaccessible stopping times include the jump times ofStopping rules in clinical trials
Clinical trials in medicine often perform interim analysis, in order to determine whether the trial has already met its endpoints. However, interim analysis create the risk of false-positive results, and therefore stopping boundaries are used to determine the number and timing of interim analysis (also known as alpha-spending, to denote the rate of false positives). At each of R interim tests, the trial is stopped if the likelihood is below a threshold p, which depends on the method used. SeeSee also
*References
Further reading
* Thomas S. Ferguson