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A.K. Basu

Bio: A.K. Basu is an academic researcher from University of Calcutta. The author has contributed to research in topics: Law of the iterated logarithm & Autoregressive model. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

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TL;DR: In this article, the authors obtained confidence sequences using Chow's generalization of the Hajek-Renyi inequality and the law of iterated logarithm for the autoregressive parameter in a stable first-order auto-regression model where innovations are assumed to be independently and identically distributed.

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TL;DR: In this paper, the authors proposed a prediction interval of the heat rate values on the basis of only EHV, keeping in mind that coal quality is one of the important (but not the only) factors that have a pronounced effect on the combustion process and hence on HR.
Abstract: The plant ‘Heat Rate’ (HR) is a measure of overall efficiency of a thermal power generating system. It depends on a large number of factors, some of which are non-measurable, while data relating to others are seldom available and recorded. However, coal quality (expressed in terms of ‘effective heat value’ (EHV) as kcal/kg) transpires to be one of the important factors that influences HR values and data on EHV are available in any thermal power generating system. In the present work, we propose a prediction interval of the HR values on the basis of only EHV, keeping in mind that coal quality is one of the important (but not the only) factors that have a pronounced effect on the combustion process and hence on HR. The underlying theory borrows the idea of providing simultaneous confidence interval (SCI) to the coefficients of a p-th p(≥1) order autoregressive model (AR(p)). The theory has been substantiated with the help of real life data from a power utility (after suitable base and scale transfo...