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・ Truncated order-5 square tiling
・ Truncated order-6 hexagonal tiling
・ Truncated order-6 octagonal tiling
・ Truncated order-6 pentagonal tiling
・ Truncated order-6 square tiling
・ Truncated order-7 heptagonal tiling
・ Truncated order-7 square tiling
・ Truncated order-7 triangular tiling
・ Truncated order-8 hexagonal tiling
・ Truncated order-8 octagonal tiling
・ Truncated order-8 triangular tiling
・ Truncated pentahexagonal tiling
・ Truncated pentakis dodecahedron
・ Truncated power function
・ Truncated railway station termini
Truncated regression model
・ Truncated rhombicosidodecahedron
・ Truncated rhombicuboctahedron
・ Truncated spur
・ Truncated square antiprism
・ Truncated square tiling
・ Truncated square trapezohedron
・ Truncated tesseract
・ Truncated tesseractic honeycomb
・ Truncated tetraapeirogonal tiling
・ Truncated tetrahedral prism
・ Truncated tetrahedron
・ Truncated tetraheptagonal tiling
・ Truncated tetrahexagonal tiling
・ Truncated tetraoctagonal tiling


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Truncated regression model : ウィキペディア英語版
Truncated regression model
Truncated regression models arise in many applications of statistics, for example in econometrics,〔Amemiya, T. (1973): “Regression Analysis When the Dependent Variable is Truncated Normal,” Econometrica, 41, 997–1016.〕〔Heckman, J. J. (1976): “The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for Such Models,” Annals of Economic and Social Measurement, 15, 475–492.〕〔Lewbel, A. and O. Linton, (2002), Nonparametric censored and truncated regression, Econometrica, 70, 765–779.〕〔(Park, B.U., L. Simar, and V. Zelenyuk (2008). "Local likelihood estimation of truncated regression and its partial derivatives: Theory and application," Journal of Econometrics 146(1), pages 185-198. )〕 in cases where observations with values in the outcome variable below or above certain thresholds are systematically excluded from the sample. Therefore, whole observations are missing, so that neither the dependent nor the independent variable is known.
Truncated regression models are often confused with censored regression models where only the value of the dependent variable is clustered at a lower threshold, an upper threshold, or both, while the value for independent variables is available.
==Example==
One example of truncated samples come from historical military height records. Many armies imposed a minimum height requirement (MHR) on soldiers. This implies that men shorter than the MHR are not included in the sample. This implies that samples drawn from such records are perforce deficient i.e., incomplete, inasmuch as a substantial portion of the underlying population's height distribution is unavailable for analysis. Consequently, without proper statistical correction, any results obtained from such deficient samples, such as means, correlations, or regression coefficients are wrong (biased). In such a case truncated regression has the considerable advantage of immediately providing consistent and unbiased estimates of the coefficients of the independent variables, as well as their standard errors, thereby allowing for further statistical inference, such as the calculation of the ''t''-values of the estimates.

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