Statistical models for market segmentation

It is an essential element of market research that customer preferences are considered and the heterogeneity of these preferences is recognized. By segmenting the market into homogeneous clusters the preferences of customers is addressed. Latent class methodology for conjoint analysis, proposed by Green (2000), is one of the several conjoint segmentation procedures that overcome the limitations of aggregate analysis and priori segmentation. This approach proposes the proportional odds model as a proper statistical model for ordinal categorical data in which the item attributes are included in the linear predictor. The likelihood is maximized through the EM algorithm. This paper considers two extensions of this methodology that incorporate individual characteristics into the models.peer-reviewe

Statistical Modelling of Cell Movement

In this paper we demonstrate an application of the unscented Kalman filter in the context of cell movement, using a model defined in terms of stochastic differential equations (SDEs)

Combining information in statistical modelling

How to combine information from different sources is becoming an important statistical area of research under the name of Meta Analysis. This paper shows that the estimation of a parameter or the forecast of a random variable can also be seen as a process of combining information. It is shown that this approach can provide sorne useful insights on the robustness properties of sorne statistical procedures, and it also allows the comparison of statistical models within a common framework. Sorne general combining rules are illustrated using examples from ANOVA analysis, diagnostics in regression, time series forecasting, missing value estimation and recursive estimation using the Kalman Filter

Statistical modelling of international migration flows

The paper deals with uncertainty in estimating international migration flows for an interlinked system of countries. The related problems are discussed on the example of a dedicated model 'IMEM' (Integrated Model of European Migration). The IMEM is a hierarchical Bayesian model, which allows for combining data from different countries with meta-data on definitions and collection methods, as well as with relevant expert information. The model is applied to 31 EU and EFTA countries for the period 2002–2008. The expert opinion comes from a two-round Delphi survey carried out amongst 11 European experts on issues related to migration statistics. The adopted Bayesian approach allows for a coherent quantification of uncertainty stemming from different sources (data discrepancies, model parameters, and expert judgement). The outcomes produced by the model – whole posterior distributions of estimated flows – can be then used for assessing the true magnitude of flows at the European level, taking into account relative costs of overestimating or underestimating of migration flows. In this context, problems related to application of the decision statistical analysis to multidimensional problems are briefly discusse

Statistical modelling of software reliability

During the six-month period from 1 April 1991 to 30 September 1991 the following research papers in statistical modeling of software reliability appeared: (1) A Nonparametric Software Reliability Growth Model; (2) On the Use and the Performance of Software Reliability Growth Models; (3) Research and Development Issues in Software Reliability Engineering; (4) Special Issues on Software; and (5) Software Reliability and Safety

Multivariate statistical modelling of future marine storms

Extreme events, such as wave-storms, need to be characterized for coastal infrastructure design purposes. Such description should contain information on both the univariate behaviour and the joint-dependence of storm-variables. These two aspects have been here addressed through generalized Pareto distributions and hierarchical Archimedean copulas. A non-stationary model has been used to highlight the relationship between these extreme events and non-stationary climate. It has been applied to a Representative Concentration Pathway 8.5 Climate-Change scenario, for a fetch-limited environment (Catalan Coast). In the non-stationary model, all considered variables decrease in time, except for storm-duration at the northern part of the Catalan Coast. The joint distribution of storm variables presents cyclical fluctuations, with a stronger influence of climate dynamics than of climate itself.Peer ReviewedPostprint (author's final draft

Statistical Modelling

The book collects the proceedings of the 19th International Workshop on Statistical Modelling held in Florence on July 2004. Statistical modelling is an important cornerstone in many scientific disciplines, and the workshop has provided a rich environment for cross-fertilization of ideas from different disciplines. It consists in four invited lectures, 48 contributed papers and 47 posters. The contributions are arranged in sessions: Statistical Modelling; Statistical Modelling in Genomics; Semi-parametric Regression Models; Generalized Linear Mixed Models; Correlated Data Modelling; Missing Data, Measurement of Error and Survival Analysis; Spatial Data Modelling and Time Series and Econometrics

Managing uncertainty:financial, actuarial and statistical modelling.

present value; Value; Actuarial;