Accelerated Life Models: Modeling and Statistical Analysis by Vilijandas Bagdonavicius, Mikhail Nikulin

By Vilijandas Bagdonavicius, Mikhail Nikulin

The authors of this monograph have built a wide and demanding category of survival research versions that generalize many of the present types. In a unified, systematic presentation, this monograph totally info these types and explores parts of speeded up existence checking out frequently basically touched upon within the literature.Accelerated lifestyles versions: Modeling and Statistical research provides types, equipment of information assortment, and statistical research for failure-time regression information in sped up lifestyles checking out and for degradation facts with explanatory variables. as well as the classical effects, the authors dedicate enormous awareness to types with time-varying explanatory variables and to tools of semiparametric estimation. in addition they study the simultaneous research of deterioration and failure-time info while the intensities of failure in several modes depend upon the extent of decay and the values of explanatory variables.The authors stay away from technical information via explaining the guidelines and touching on assets the place thorough research are available. no matter if used for educating, examine or basic reference, sped up lifestyles types: Modeling and Statistical research offers new and recognized versions and glossy equipment of sped up lifestyles information research.

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14. The PH and AFT models are equivalent on E1 if and only if the failure-time distribution is Weibull for all x ∈ E1 Proof. Sufficiency. If the failure-time distribution is Weibull and the PH model holds on E1 then for all x ∈ E1 t Sx (t) = e−( θ(x) ) α(x) = S0 (t)r(x) , Taking twice the logarithm of both sides, we obtain that for all t > 0 α(x)(ln t − ln θ(x)) = ln r(x) + ln(− ln S0 (t)). e. the AFT model holds on E1 . If the failure-time distribution is Weibull and the AFT model holds then t α Sx (t) = e−( θ(x) ) , Ax (t) = ( t α ) , θ(x) αx (t) = r(x) α0 (t), −α and α0 (t) = tα .

43) For any t the hazard rate under the time-varying stress x(·) at the moment t does not depend on the values of the stress x(·) before the moment t but only on the value of it at this moment. e. when times-to-failure are not exponential under constant stresses. 5) can be written as the usual PH model with constant coefficients and time-varying ”explanatory variables”. Note also that the PH model can be considered as a conditional model when heterogeneity of units is observed. For example, if the AFT model with the exponential distribution under constant stresses holds on a set E = E (1) ×E (2) of stresses of the form (x(1) (·), x(2) (·)): αx(·) (t) = r1 {x1 (t)} r2 {x2 (t)}, then conditionally, given a fixed time-varying component x(2) (·), the PH model holds on the set E (1) : αx(1) (·) (t) = r1 {x1 (t)} α0 (t), where α0 (t) = r2 {x2 (t)}.

7 The main classes of the GPH models Particular classes of the GPH models are important for survival analysis and accelerated life testing. The numerous examples of real data show that taking two constant in time covariates, say x1 and x2 , the ratio αx2 (t)/αx1 (t) (which is constant under the PH model), can be increasing or decreasing in time and even a cross-effect of hazard rates can be observed. Such data can be modeled by sub-models of the GPH1 or more general GPH2 model. Let us consider some of them.

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