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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Diabetes Prediction In Pima Indians Using Ann And Statistical Techniques, Kuldeep Kumar, Ping Zhang
Diabetes Prediction In Pima Indians Using Ann And Statistical Techniques, Kuldeep Kumar, Ping Zhang
Kuldeep Kumar
Due to the fact that Pima Indian tribe has lived in the same location for an unmitigated number of years, a vast source of information of these people has been gained, which helps researchers for the study of diabetes and possible genetic factors of the disease. In this paper, we use Artificial Neural Network (ANN) and some statistical techniques for the prediction of diabetes. All the prediction models are evaluated with ROC curves.
How To Make Teaching Of Statistics More Effective In Business Schools?, Kuldeep Kumar
How To Make Teaching Of Statistics More Effective In Business Schools?, Kuldeep Kumar
Kuldeep Kumar
Statistics is taught in almost all Business Schools as a core course and prerequisite to may advance economics, finance and accountancy courses. However, Statistics has to be taught in a different way in Business Schools as compared to how it is taught in their own statistics department. There should be more emphasis on applications in Business area rather than theory. There has been lot of interest in teaching of statistics in Business schools for a very long time, for example see Cox (1965), Moore (1976) and Love and Hildebrand (2002). This paper discusses author's experience of teaching statistics in an …
A Brief Review Of Recent Research Trends On Applications Of Computational And Statistical Techniques In Financial & Business Intelligence, Kuldeep Kumar, Sukanto Bhattacharya
A Brief Review Of Recent Research Trends On Applications Of Computational And Statistical Techniques In Financial & Business Intelligence, Kuldeep Kumar, Sukanto Bhattacharya
Kuldeep Kumar
Artificial neural networks and statistical techniques like decision trees,discriminant analysis, logistic regression and survival analysis play a crucial role in Business Intelligence. These predictive analytical tools exploit patterns found in historical data to make predictions about future events. In this paper we have shown some recent developments of a few of these techniques in financial and business intelligence applications like fraud detection, bankruptcy prediction and credit rating scoring.
Bayesian Mixtures Of Autoregressive Models, Sally Wood, Ori Rosen, Robert Kohn
Bayesian Mixtures Of Autoregressive Models, Sally Wood, Ori Rosen, Robert Kohn
Sally Wood
In this paper we propose a class of time-domain models for analyzing possibly nonstationary time series. This class of models is formed as a mixture of time series models, whose mixing weights are a function of time. We consider specifically mixtures of autoregressive models with a common but unknown lag. The model parameters, including the number of mixture components, are estimated via Markov chain Monte Carlo methods. The methodology is illustrated with simulated and real data.
Rejoinder: Estimation Issues For Copulas Applied To Marketing Data, Peter Danaher, Michael Smith
Rejoinder: Estimation Issues For Copulas Applied To Marketing Data, Peter Danaher, Michael Smith
Michael Stanley Smith
Estimating copula models using Bayesian methods presents some subtle challenges, ranging from specification of the prior to computational tractability. There is also some debate about what is the most appropriate copula to employ from those available. We address these issues here and conclude by discussing further applications of copula models in marketing.
Forecasting Television Ratings, Peter Danaher, Tracey Dagger, Michael Smith
Forecasting Television Ratings, Peter Danaher, Tracey Dagger, Michael Smith
Michael Stanley Smith
Despite the state of flux in media today, television remains the dominant player globally for advertising spend. Since television advertising time is purchased on the basis of projected future ratings, and ad costs have skyrocketed, there is increasing pressure to forecast television ratings accurately. Previous forecasting methods are not generally very reliable and many have not been validated, but more distressingly, none have been tested in today’s multichannel environment. In this study we compare 8 different forecasting models, ranging from a naïve empirical method to a state-of-the-art Bayesian model-averaging method. Our data come from a recent time period, 2004-2008 in …
An Autoregressive Approach To House Price Modeling, Chaitra Nagaraja, Lawrence Brown, Linda Zhao
An Autoregressive Approach To House Price Modeling, Chaitra Nagaraja, Lawrence Brown, Linda Zhao
Chaitra H Nagaraja
No abstract provided.