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on Econometrics |
| By: | Sara Lopez-Pintado; Juan Romo |
| Abstract: | Classification is an important task when data are curves. Recently, the notion of statistical depth has been extended to deal with functional observations. In this paper, we propose robust procedures based on the concept of depth to classify curves. These techniques are applied to a real data example. An extensive simulation study with contaminated models illustrates the good robustness properties of these depth-based classification methods. |
| Date: | 2005–10 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws055611&r=ecm |
| By: | Junyi Shen (Osaka School of International Public policy, Osaka University) |
| Abstract: | This paper reviews Stated Choice Method (SCM), paying particular attentions on its theoretical background, application, empirical models, experimental design, and procedure to execute. The review suggests that comparing to other stated preference (SP) methods, SCM has a major advantage that it meets the objective of a stated preference analysis to simulate actual consumer behavior by allowing simultaneous evaluations of a number of alternatives or a choice between alternatives. Some advanced models based on the degrees of relaxation of the Independently and Identically Distributed (IID) assumption on error terms are introduced. More complex model seems to be more plausible than relatively simple specifications. Two tests for nested and non-nested models are also discussed to help judge that one model is superior to another model. Finally, this paper introduces the procedure of executing a Stated Choice (SC) experiment. |
| Keywords: | Stated Choice Method (SCM), Stated Preference (SP) method, Independent and Identical Distribution (IID), Extreme Value type I (EV1) distribution |
| JEL: | C35 C81 C93 |
| Date: | 2005–10 |
| URL: | http://d.repec.org/n?u=RePEc:osk:wpaper:0527&r=ecm |
| By: | Michael Greenacre; Oleg Nenadic |
| Abstract: | The generalization of simple correspondence analysis, for two categorical variables, to multiple correspondence analysis where they may be three or more variables, is not straighforward, both from a mathematical and computational point of view. In this paper we detail the exact computational steps involved in performing a multiple correspondence analysis, including the special aspects of adjusting the principal inertias to correct the percentages of inertia, supplementary points and subset analysis. Furthermore, we give the algorithm for joint correspondence analysis where the cross-tabulations of all unique pairs of variables are analysed jointly. The code in the R language for every step of the computations is given, as well as the results of each computation. |
| Keywords: | Adjustment of principal inertias, Burt matrix, correspondence analysis, multiple correspondence analysis, R language, singular value decomposition, subset analysis |
| JEL: | C19 C88 |
| Date: | 2005–09 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:887&r=ecm |