Increasing cost of soliciting customers along with amplified efforts to improve the bottom-line amidst intense competition is driving the firms to rely on more cutting edge analytic methods by leveraging the knowledge of customer-base that is allowing the firms to engage better with customers by offering right product/service to right customer. Increased interest of the firms to engage better with their customers has evidently resulted into seeking answers to the key question: Why are customers likely to respond? in contrast to just seek answers for question: Who are likely to respond? This has resulted in developing propensity based response models that have become a center stage of marketing across customer life cycle. Propensity based response models are used to predict the probability of a customer or prospect responding to some offer or solicitation and also explain the drivers– why the customers are likely to respond. The output from these models will be used to segment markets, to design strategies, and to measure marketing performance. In our present paper we will use support vector machines and Logistic Regression to build propensity based response models and evaluate their performance.
Abstract:We propose a new lifetime model, called the Odd Generalized Exponential Type-I Generalised Half Logistic Distribution (OGET-IGHLD).We obtain some of its mathematical properties. We made an attempt to study some structural properties of the new distribution. The Maximum Likelihood method is used for Estimating the model parameters and the Fisher's information matrix is also derived. We illustrate the applications with generalised data.Keywords: Type-I Generalised Half Logistic Distribution, Hazard Function, Moments, Quantile, Maximum Likelihood, Estimation, Information Martix.
Introduction:In life testing and reliability studies a combination of monotone and constant failure rates over various segments of the range of lifetime of a random variable is known as a bathtub or a non-monotone failure rate. In the biological and engineering sciences there are situations of non-monotone failure rates available to model such data; a comprehensive narration of the models is given in Rajarshi &Rajarshi (1988). Mudholkar, et al. (1995) presented an extension of the Weibull family that contains unimodel distributions with bathtub failure rates and also allows for a broader class of monotone hazard rates; they named their extended version the Exponentiated Weibull Family. Gupta and Kundu (1999) and the corresponding probability distribution may be termed exponentiated or generalized versions of F(x).This generalization is adapted to the half logistic distribution and the resulting model is considered in this study. A half logistic model obtained as the distribution of an absolute standard logistic variate is a probability model of recent origin (Balakrishnan, 1985). Some well-known generators are the Marshall-Olkin generated (MO-G) by Marshall and Olkin (1997), The properties of Exp-G distributions have been studied by many authors in recent years, see Mudholkar and Srivastava (1993).There are always urge among the researchers for developing new and more flexible distributions. As a result, many new distributions have come up and studied. Recently, Tahir et al. (2015) propose a new class of distributions called the odd generalized exponential (OGE) family and study each of the OGE-Weibull (OGE-W) distribution, the OGE-Fréchet (OGE-Fr)distribution and the OGE-Normal (OGE-N) distribution. These models are flexible because of the hazard shapes: increasing, decreasing, bathtub and upside subset of down bathtub. A random variable X is said to have generalized exponential (GE) distribution with parameters α, β if the cumulative distribution function (CDF) is given by
It is assumed that the probabilistic model of the quality characteristics follows the new weighted exponential distribution. Control charts based on each subgroup's extreme values are established. The constants in the control chart are determined by the probability distribution of the extreme value order statistics of the sub-group and the sub-group size. The proposed chart is thus referred to as an extreme values chart. A biased overall mean analysis method (ANOM for truncated population) is used for the Lomax Distribution. Examples based on real time data are used to explain the findings. Keywords: ANOM, Equi-tailed, In-control, LD.
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