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Study of Binary Skew LOGIT Model and Its Application

Author: WeiGang
Tutor: LiuYingZhou；BianKuanJiang
School: Northwest University of Science and Technology
Course: Applied Mathematics
Keywords: Binary choice model Binary Probit model The two yuan Logit model The two yuan partial Logit model Parameter estimation
CLC: O212.1
Type: Master's thesis
Year: 2011
Downloads: 119
Quote: 0
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Abstract


In all statistical models in the form of a linear model is the most simple, the easiest to deal with, but also the most basic, the most effective statistical model of the application, it tends to become the first choice of the statistical model. However, the response between the linearity assumption and explanatory variables often does not hold in many cases. An idea to solve this problem: the appropriate adjustments to the linear model, while retaining the original linear hypothesis. Example: binary Probit model and binary Logit model is the successful practice of the idea. In the linear hypothesis can not be retained under the premise Another idea is to solve this problem: direct non-linear model regression analysis. Binary choice model originated in the statistical treatment of the biological experiments. However, with the development of the model, it has been gradually breaking away from the shackles of the biological experiments, experienced linear models (linear probability model), and ultimately develop into independent generalized linear models, namely the binary Probit model and binary Logit model. They retain the original linear assumption to make proper adjustments on the original linear model based on. Both have good statistical properties, so the two have become the most popular and one of the most widely used statistical models. This article is from the generation of the binary choice model, briefly introduced the binary Probit model and binary Logit model the process of development and evolution, as well as other major S-shaped curve, research and application situation; major binary choice model and its parameter estimation method to do a summary narrative; improved binary Logit model defines a the two yuan bias Logit regression model is given and proved the curve the nature of the model, where you can see the binary Logit model can be seen for the two yuan bias Logit model θ = 1 when a special case, in theory, shows the the two yuan partial Logit model fitting forecast greater flexibility, thus generally higher fitting accuracy. Given parameter estimation method in the case of repeated observations available and not available two; Finally, by means of SPSS and Matlab software, application binary Logit model and binary partial Logit model it in loans to farmers information to determine whether the performance The research results show that the achieved good results, but the the two yuan partial Logit model fitting in can get higher accuracy than binary Logit model.

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CLC: > Mathematical sciences and chemical > Mathematics > Probability Theory and Mathematical Statistics > Mathematical Statistics > General mathematical statistics
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