In probability, statistics and related fields, the geometric process is a counting process, introduced by Lam in 1988.[1] It is defined as
The geometric process. Given a sequence of non-negative random variables : \( {\displaystyle \{X_{k},k=1,2,\dots \}} \), if they are independent and the cdf of : \( X_k \) is given by\( {\displaystyle F(a^{k-1}x)} \) for\( {\displaystyle k=1,2,\dots } \), where a is a positive constant, then : \( {\displaystyle \{X_{k},k=1,2,\ldots \}} \) is called a geometric process (GP).
The GP has been widely applied in reliability engineering [2]
Below are some of its extensions.
The α- series process.[3] Given a sequence of non-negative random variables :: \( {\displaystyle \{X_{k},k=1,2,\dots \}} \), if they are independent and the cdf of : \({\displaystyle {\frac {X_{k}}{k^{a}}}} \) is given by F(x) for : \( {\displaystyle k=1,2,\dots } \), where a is a positive constant, then : \( {\displaystyle \{X_{k},k=1,2,\ldots \}} \) is called an α- series process.
The threshold geometric process.[4] A stochastic process: \( {\displaystyle \{Z_{n},n=1,2,\ldots \}} \) is said to be a threshold geometric process (threshold GP), if there exists real numbers \( {\displaystyle a_{i}>0,i=1,2,\ldots ,k} \) and integers\( {\displaystyle \{1=M_{1}<M_{2}<\cdots <M_{k}<M_{k+1}=\infty \}} \) such that for each: \( {\displaystyle i=1,\ldots ,k} \), : \( {\displaystyle \{a_{i}^{n-M_{i}}Z_{n},M_{i}\leq n<M_{i+1}\}} \) forms a renewal process.
The doubly geometric process.[5] Given a sequence of non-negative random variables : : \( {\displaystyle \{X_{k},k=1,2,\dots \}} \), if they are independent and the cdf of \( X_k \) is given by : \( {\displaystyle F(a^{k-1}x^{h(k)})} \) for\( {\displaystyle k=1,2,\dots } \), where a is a positive constant and h(k) is a function of k and the parameters in h(k) are estimable, and \( {\displaystyle h(k)>0} \) for natural number k, then : \( {\displaystyle \{X_{k},k=1,2,\ldots \}} \) is called a doubly geometric process (DGP).
The semi-geometric process.[6] Given a sequence of non-negative random variables : \( {\displaystyle \{X_{k},k=1,2,\dots \}} \), if : \( {\displaystyle P\{X_{k}<x|X_{k-1}=x_{k-1},\dots ,X_{1}=x_{1}\}=P\{X_{k}<x|X_{k-1}=x_{k-1}\}} \) and the marginal distribution of : \( X_k \) is given by : \( {\displaystyle P\{X_{k}<x\}=F_{k}(x)(\equiv F(a^{k-1}x))} \), where a is a positive constant, then : \( {\displaystyle \{X_{k},k=1,2,\dots \}} \) is called a semi-geometric process
References
Lam, Y. (1988). Geometric processes and replacement problem. Acta Mathematicae Applicatae Sinica. 4, 366–377
Lam, Y. (2007). Geometric process and its applications. World Scientific, Singapore MATH. ISBN 978-981-270-003-2.
Braun, W. J., Li, W., & Zhao, Y. Q. (2005). Properties of the geometric and related processes. Naval Research Logistics (NRL), 52(7), 607–616.
Chan, J.S., Yu, P.L., Lam, Y. & Ho, A.P. (2006). Modelling SARS data using threshold geometric process. Statistics in Medicine. 25 (11): 1826–1839.
Wu, S. (2017). Doubly geometric processes and applications. Journal of the Operational Research Society, 1–13. doi:10.1057/s41274-017-0217-4.
Wu, S., Wang, G. (2017). The semi-geometric process and some properties. IMA J Management Mathematics, 1–13.
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