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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Tobit model ： ウィキペディア英語版 Tobit model The Tobit model is a statistical model proposed by James Tobin (1958) to describe the relationship between a non-negative dependent variable $y\_i$ and an independent variable (or vector) $x\_i$. The term ''Tobit'' was derived from Tobin's name by truncating and adding ''-it'' by analogy with the probit model.〔''International Encyclopedia of the Social Sciences'' (2008)〕 The model supposes that there is a latent (i.e. unobservable) variable $y\_i^$ *. This variable linearly depends on $x\_i$ via a parameter (vector) $\backslash beta$ which determines the relationship between the independent variable (or vector) $x\_i$ and the latent variable $y\_i^$ * (just as in a linear model). In addition, there is a normally distributed error term $u\_i$ to capture random influences on this relationship. The observable variable $y\_i$ is defined to be equal to the latent variable whenever the latent variable is above zero and zero otherwise. : $y\_i\; =\; \backslash begin$ y_i^ * & \textrm \; y_i^ * >0 \\ 0 & \textrm \; y_i^ * \leq 0 \end where $y\_i^$ * is a latent variable: : $y\_i^$ * = \beta x_i + u_i, u_i \sim N(0,\sigma^2) \, ==Consistency== If the relationship parameter $\backslash beta$ is estimated by regressing the observed $y\_i$ on $x\_i$, the resulting ordinary least squares regression estimator is inconsistent. It will yield a downwards-biased estimate of the slope coefficient and an upward-biased estimate of the intercept. Takeshi Amemiya (1973) has proven that the maximum likelihood estimator suggested by Tobin for this model is consistent. 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「Tobit model」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.