Author
Nil Kamal Hazra
Other affiliations: Indian Statistical Institute, University of the Free State, Indian Institute of Science Education and Research, Kolkata ...read more
Bio: Nil Kamal Hazra is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Stochastic ordering & Series (mathematics). The author has an hindex of 10, co-authored 44 publications receiving 342 citations. Previous affiliations of Nil Kamal Hazra include Indian Statistical Institute & University of the Free State.
Papers
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TL;DR: The conditions under which a function defined by ?
64 citations
TL;DR: This work considers the location-scale family of distributions, which contains many standard lifetime distributions, and gives conditions under which the largest order statistic of a set of random variables with different/the same location as well as different/ the same scale parameters dominates that of another set ofrandom variables with respect to various stochastic orders.
49 citations
TL;DR: In this article, the authors consider stochastic comparisons of minimum order statistics from the location-scale family of distributions that contain most of the popular lifetime distributions and show that the minimum order statistic of one set of random variables dominates that of another set of variables with respect to different orders.
Abstract: We consider stochastic comparisons of minimum order statistics from the location–scale family of distributions that contain most of the popular lifetime distributions. Under certain assumptions, we show that the minimum order statistic of one set of random variables dominates that of another set of random variables with respect to different stochastic orders. Furthermore, we illustrate our results using some well-known specific distributions.
30 citations
TL;DR: It is shown that redundancy at the component level is superior to that at the system level with respect to different stochastic orders, for different types of systems.
Abstract: Stochastic orders are useful to compare the lifetimes of two systems. We discuss both active redundancy as well as standby redundancy. We show that redundancy at the component level is superior to that at the system level with respect to different stochastic orders, for different types of systems.
26 citations
TL;DR: The theory of stochastic orders and majorization orders is used to prove that for a series system, the optimal (maximal) reliability is achieved by drawing all items from one substock, whereas, for a parallel system,The optimal solution results in an independent drawing of all Items from the whole mixed population.
21 citations
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TL;DR: This book aims to introduce simulation techniques for practitioners in the financial and risk management industry at an intermediate level by having extensive simulation examples using S–PLUS or Visual Basics.
Abstract: (2007). Stochastic Ageing and Dependence for Reliability. Technometrics: Vol. 49, No. 2, pp. 222-222.
314 citations
Journal Article•
TL;DR: In this article, a δ -shock maintenance model for a deteriorating system is studied, where shocks arrive according to a renewal process, and the interarrival time of shocks has a Weibull distribution or gamma distribution.
Abstract: Abstract In this paper, a δ -shock maintenance model for a deteriorating system is studied. Assume that shocks arrive according to a renewal process, the interarrival time of shocks has a Weibull distribution or gamma distribution. Whenever an interarrival time of shocks is less than a threshold, the system fails. Assume further the system is deteriorating so that the successive threshold values are geometrically nondecreasing, and the consecutive repair times after failure form an increasing geometric process. A replacement policy N is adopted by which the system will be replaced by an identical new one at the time following the N th failure. Then the long-run average cost per unit time is evaluated. Afterwards, an optimal policy N * for minimizing the long-run average cost per unit time could be determined numerically.
72 citations
TL;DR: The present study adds to the previous literature surveys and focuses mainly on papers after year 2000 but with a quick review on the previous works so that the readers become familiar with the existing approaches.
Abstract: Article history: Received September 22 2013 Received in Revised Format April 6 2014 Accepted May 18 2014 Available online May 25 2014 The purpose of this paper is to discuss the state of the art on models and methods for reliability optimization problems (ROPs) including reliability allocation, redundancy allocation and reliability-redundancy allocation. There are literally few surveys for reviewing the literature of the ROPs. Tillman et al. (1980) classified the related papers by system structure, problem type, and solution methods, separately. In another work, Tzafestas (1980) reviewed system reliability optimization models and the optimization techniques. Yearout (1986) reviewed the literature related to standby redundancy. Kuo (2000) studied the system reliability optimization based on system structure and solution methods. Kuo and Prasad (2004) overviewed system reliability optimization methods. Later, Kuo (2007) reviewed recent advances in optimal reliability allocation problems. The present study adds to the previous literature surveys and focuses mainly on papers after year 2000 but with a quick review on the previous works so that the readers become familiar with the existing approaches. This research investigates the literature from system structure, system performance, uncertainty state and solution approach standpoints, simultaneously.
65 citations