Aditya Chatterjee

Warranty forecasting from incomplete two-dimensional warranty data

Warranty modelling with incomplete data is a major issue in reliability analysis. The incomplete failure region characterized by warranty field data may be classified into several domains representing failures from manufacturing/assembly defects, usage or fatigue. In the present paper a data driven approach has been suggested to demark the regions optimally through estimation of the change point in a hazard function. In the perspective of bivariate warranty analysis, as relevant in automobiles, we have assumed the lifetime distribution to be a mixture of distributions corresponding to the burn-in period and the useful life period. The proportions of observations in different regions demarketed by the warranty policy in the bivariate plane have been estimated by considering mileage along with age. The estimation scheme has been verified and validated through extensive simulation studies. The utilities of the results have been demonstrated by addressing several issues through a real life synthetic warranty data set from a large automobile company.

Some reliability issues for incomplete two-dimensional warranty claims data

Bivariate reliability and vector bivariate hazard rate or hazard gradient functions are expected to have a role for meaningful assessment of the field performance for items under two-dimensional warranty coverage. In this paper a usage rate based simple class of bivariate reliability function is proposed and various bivariate reliability characteristics are studied for warranty claims data. The utilities of such study are explored with the help of a real life synthetic data.

Prioritisation of the determinants of customer satisfaction: A simultaneous equation approach in ordinal endogenous set-up

The determination and ordering of the influencers of customer satisfaction are of paramount interest in various service industries. The theory of logistic regression may be exploited to relate customer satisfaction usually measured in an ordinal scale with possible covariates, measured in metrical, ordinal or binary scales. However, some of the confounders are themselves determined by other covariates under study. This necessitates the use of a simultaneous equation with ordinal endogenous variables. We propose one such approach and demonstrate its efficacy with a real life example.

Improving variable neighborhood search to solve the traveling salesman problem

Abstract The traveling salesman problem (TSP) is one of the classical combinatorial optimization problems and has wide application in various fields of science and technology. In the present paper, we propose a new algorithm for solving the TSP that uses the variable neighborhood search (VNS) algorithm coupled with a stochastic approach for finding the optimal solution. Such neighborhood search with various other local search algorithms, named as VNS-1 and VNS-2, has been reported in the literature. The proposed algorithm is compared in detail with these algorithms, in the light of two benchmark TSP problems (one being symmetric while the other is asymmetric) suggested in the TSPLIB dataset in programming language R , along with two asymmetric problems obtained through simulation experiment. The present algorithm has been found to perform better than the conventional algorithms implemented in R for solving TSPs, and also, on an average, found to be more effective than the VNS-1 and the VNS-2 algorithms. The performance of the proposed algorithm has also been tested on 60 benchmark symmetric TSPs from the TSPLIB dataset. Apart from solving the TSP, the flexibility of the proposed hybrid algorithm to solve some other optimization problems related to other disciplines has also been discussed.

On Optimal Allocation of Experimental Units with Known Covariates into Multiple Treatment Groups

Finding the optimal design for allocating two or more treatments to a fixed group of experimental units with several known covariates is an important problem in many studies. With the objective of efficient estimation of the treatment effect or the covariate effects or both with regard to the well-known D- and or A- D s - and A s - optimalities, as well as some other robustness criteria, Hore et al.[1] considered this allocation problem in case of two treatments. In the present article, the method has been extended to r(≥2) treatments and the proposed design has been compared with several other allocation rules available in the literature including the most popular one, the randomized allocation rule. It is to be noted that finding the exact optimal allocation design with the above objective is computationally intractable in case of large number of experimental units having information on multiple covariates. By generalizing the algorithm of Hore et al.[1], a near-optimum allocation design may be obtained with less computational burden. Some simulation studies and real life data analysis have been undertaken to demonstrate the efficacy of the proposed algorithm in comparison with others for the case of multiple treatments.

A powerful method to integrate genotype and gene expression data for dissecting the genetic architecture of a disease

To decipher the genetic architecture of human disease, various types of omics data are generated. Two common omics data are genotypes and gene expression. Often genotype data for a large number of individuals and gene expression data for a few individuals are generated due to biological and technical reasons, leading to unequal sample sizes for different omics data. Unavailability of standard statistical procedure for integrating such datasets motivates us to propose a two-step multi-locus association method using latent variables. Our method is powerful than single/separate omics data analysis and it unravels comprehensively deep-seated signals through a single statistical model. Extensive simulation confirms that it is robust to various genetic models as its power increases with sample size and number of associated loci. It provides p-values very fast. Application to real dataset on psoriasis identifies 17 novel SNPs, functionally related to psoriasis-associated genes, at much smaller sample size than standard GWAS.

DESIGN METHODOLOGY OF SOLAR POWERED LOAD CONTROLLER USING SENSOR ASSISTED REMOTE BATTERY STATE OF CHARGE ESTIMATOR

One of the main problems of a solar PV plant is low generation of electricity during bad weather condition when the generated power is less than the claimed demand of power. Under this condition it is not possible to generate more electricity as per demand once the power plant is designed. When the supply of electricity to the consumers reduces drastically and when there is no option for manipulation of power — a blackout out or load shedding is the inevitable. Online demand regulation i.e. regulating the claimed demand of the individual consumer depending on predicted power generation is an alternate option in this constrained situation. The present paper will address this particular problem through probabilistic duration estimation power against a critical load using statistical model. Our goal is to design a predictive model considering the environmental fluctuation of solar clarity index into consideration and incorporates a policy of meeting the critical base load primarily and loads exceeding the base load secondarily depending on predicted energy generation. A modified model of MARS (Multi variate adaptive regression spline) is developed and finally used for prediction of battery state of health from a remote end using sunlight intensity (similar to clarity index) using remote sensor technology. We use Multivariate adaptive smoothing spline with adaptive smoothing features of noisy data available from light sensor.Copyright