The Taste for Variety: A Hedonic Analysis

Based on the model of consumers' variety-seeking behavior introduced by Anderson et al. (1992), this paper derives a hedonic price function for a households' consumption bundle. The price a consumer pays for her consumption bundle reflects the values of the underlying attributes of goods purchased but also accounts for any preference for variety that she might have. The empirical analysis is conducted for 3,240 German households and their expenditure on 182 different soft drinks over a six-month period. We find that consumers have a preference for variety in food consumption, ceteris paribus. Furthermore, the per-unit price is found to be significantly larger for higher income households, as well as households where the principal wage earner has a high level of education. Larger households tend to spend less on soft drinks per unit.consumer demand, taste for variety, food products, hedonic analysis, Germany, Food Consumption/Nutrition/Food Safety, C21, D12,

Experimental economics: Methods, problems and promise

The purpose of this paper is to discuss the growing importance of experimentation in economic analysis. We present a variety of economic issues that have been explored with laboratory techniques. We also address some common objections to experimentation, as well as some of the principal lessons that have been learned.

Generating Innovations in Economic Variables

Stock prices should respond only to unpredictable components of economic news (‘innovations’) in efficient markets. While innovations used in empirical investigations of the economic underpinnings of stock market risk should at least satisfy this basic requirement this may not guarantee satisfactory research results. Three methods of generating innovations are evaluated for a variety of economic variables. First differencing produces unsatisfactory serially correlated innovations in general. Both ARIMA and Kalman Filter innovations are unpredictable, but in a further evaluation the component scores from Principal Components Analysis are regressed against economic innovations using PcGets. The results are far less noisy when Kalman Filter innovations are used.Macroeconomic variables, Innovations, stock returns, principal components analysis

Three-dimensional face recognition: An Eigensurface approach

We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching

CLASSIFICATION OF FEATURE SELECTION BASED ON ARTIFICIAL NEURAL NETWORK

Pattern recognition (PR) is the central in a variety of engineering applications. For this reason, it is indeed vital to develop efficient pattern recognition systems that facilitate decision making automatically and reliably. In this study, the implementation of PR system based on computational intelligence approach namely artificial neural network (ANN) is performed subsequent to selection of the best feature vectors. A framework to determine the best eigenvectors which we named as ‘eigenpostures’ of four main human postures specifically, standing, squatting/sitting, bending and lying based on the rules of thumb of Principal Component Analysis (PCA) has been developed. Accordingly, all three rules of PCA namely the KG-rule, Cumulative Variance and the Scree test suggest retaining only 35 main principal component or ‘eigenpostures’. Next, these ‘eigenpostures’ are statistically analyzed via Analysis of Variance (ANOVA) prior to classification. Thus, the most relevant component of the selected eigenpostures can be determined. Both categories of ‘eigenpostures’ prior to ANOVA as well as after ANOVA served as inputs to the ANN classifier to verify the effectiveness of feature selection based on statistical analysis. Results attained confirmed that the statistical analysis has enabled us to perform effectively the selection of eigenpostures for classification of four types of human postures