Heterogeneous Information and Appraisal Smoothing

This study examines the heterogeneous appraiser behavior and its implication on the traditional appraisal smoothing theory. We show that the partial adjustment model is consistent with the traditional appraisal smoothing argument only when all the appraisers choose the same smoothing technique. However, if appraiser behavior is heterogeneous and exhibits cross-sectional variation due to the difference in their access to, and interpretation of information, the model actually leads to a mixed outcome: The variance of the appraisal-based returns can be higher or lower than the variance of transaction-based return depending on the degree of such heterogeneity. Using data from the residential market, we find that, contrary to what the traditional appraisal smoothing theory would predict, appraisal-based indices may not suffer any “smoothing” bias. These findings suggest that the traditional appraisal smoothing theory, which fails to consider the heterogeneity of appraiser behaviors, exaggerates the effect of appraisal smoothing.

High accuracy handwritten Chinese character recognition using quadratic classifiers with discriminative feature extraction

http://ieeexplore.ieee.orghttp://ieeexplore.ieee.orgWe aim to improve the accuracy of handwritten Chinese character recognition using two advanced techniques: discriminative feature extraction (DFE) and discriminative learning quadratic discriminant function (DLQDF). Both methods are based on the minimum classification error (MCE) training method of Juang et al. [7], and we propose to accelerate the training process on large category set using hierarchical classification. Our experimental results on two large databases show that while the DFE improves the accuracy significantly, the DLQDF improves only slightly. Compared to the modified quadratic discriminant function (MQDF) with Fisher discriminant analysis, the error rates on two test sets were reduced by factors of 29.9% and 20.7%, respectively