翻訳と辞書 Words near each other ・ Mixed government ・ Mixed Grand Lodge of France ・ Mixed graph ・ Mixed grass prairie ・ Mixed grill ・ Mixed Group ・ Mixed hockey ・ Mixed In Key ・ Mixed inhibition ・ Mixed Kebab ・ Mixed Kočevje subdialects ・ Mixed language ・ Mixed layer ・ Mixed linear complementarity problem ・ Mixed liquor suspended solids ・ Mixed logit ・ Mixed lymphocyte reaction ・ Mixed Magic ・ Mixed Magics ・ Mixed marriage ・ Mixed Marriage (play) ・ Mixed martial arts ・ Mixed martial arts clothing ・ Mixed martial arts competition for children ・ Mixed martial arts in Australia ・ Mixed martial arts in Ontario ・ Mixed Martial Arts Pakistan ・ Mixed martial arts rules ・ Mixed martial arts weight classes ・ Mixed mating model Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Mixed logit ： ウィキペディア英語版 Mixed logit Mixed logit is a fully general statistical model for examining discrete choices. The motivation for the mixed logit model arises from the limitations of the standard logit model. The standard logit model has three primary limitations, which mixed logit solves: "It (Logit ) obviates the three limitations of standard logit by allowing for random taste variation, unrestricted substitution patterns, and correlation in unobserved factors over time."〔(Train, K. (2003) Discrete Choice Methods with Simulation )〕 Mixed logit can also utilize any distribution for the random coefficients, unlike probit which is limited to the normal distribution. It has been shown that a mixed logit model can approximate to any degree of accuracy any true random utility model of discrete choice, given an appropriate specification of variables and distribution of coefficients."〔McFadden, D. and Train, K. (2000). “(Mixed MNL Models for Discrete Response ),” Journal of Applied Econometrics, Vol. 15, No. 5, pp. 447-470,〕 The following discussion draws from (Ch. 6 ) of (Discrete Choice Methods with Simulation ), by Kenneth Train (Cambridge University Press), to which the reader is referred for more details and citations. See also the article on discrete choice for information on how the mixed logit relates to discrete choice analysis in general and to other specific types of choice models. ==Random taste variation== The standard logit model's "taste" cofficients, or $\backslash beta$'s, are fixed, which means the $\backslash beta$'s are the same for everyone. Mixed logit has different $\backslash beta$'s for each person (i.e., each decision maker.) In the standard logit model, the utility of person n for alternative i is: :$U\_\; =\; \backslash beta\; x\_\; +\; \backslash varepsilon\_$ with :$\backslash varepsilon\_$ ~ iid extreme value For the mixed logit model, this specification is generalized by allowing $\backslash beta\_n$ to be random. The utility of person n for alternative i in the mixed logit model is: :$U\_\; =\; \backslash beta\_n\; x\_\; +\; \backslash varepsilon\_$ with :$\backslash varepsilon\_$ ~ iid extreme value :$\backslash quad\; \backslash beta\_n\; \backslash sim\; f(\backslash beta\; |\; \backslash theta)$ where ''θ'' are the parameters of the distribution of $\backslash beta\_n$'s over the population, such as the mean and variance of $\backslash beta\_n$. Conditional on $\backslash beta\_n$, the probability that person n chooses alternative i is the standard logit formula: :$L\_\; (\backslash beta\_)\; =\; \backslash frac\}\}\; X\_\}\}$ However, since $\backslash beta\_n$ is random and not known, the (unconditional) choice probability is the integral of this logit formula over the density of $\backslash beta\_n$. :$P\_\; =\; \backslash int\; L\_\; (\backslash beta)\; f(\backslash beta\; |\; \backslash theta)\; d\backslash beta$ This model is also called the random coefficient logit model since $\backslash beta\_n$ is a random variable. It allows the slopes of utility (i.e., the marginal utility) to be random, which is an extension of the random effects model where only the intercept was stochastic. Any probability density function can be specified for the distribution of the coefficients in the population, i.e., for $f(\backslash beta\; |\; \backslash theta)$. The most widely used distribution is normal, mainly for its simplicity. For coefficients that take the same sign for all people, such as a price coefficient that is necessarily negative or the coefficient of a desirable attribute, distributions with support on only one side of zero, like the lognormal, are used.〔David Revelt and Train, K (1998). "(Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level )," Review of Economics and Statistics, Vol. 80, No. 4, pp. 647-657〕〔Train, K (1998)."(Recreation Demand Models with Taste Variation )," Land Economics, Vol. 74, No. 2, pp. 230-239.〕 When coefficients cannot logically be unboundedly large or small, then bounded distributions are often used, such as the $S\_b$ or triangular distributions. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Mixed logit」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.