Nengxiang Ling
Abstract
Considering semiparametric regression model with a liner process enors Y_(ni0=(β·t_(ni0+g(x_(ni0)+e_(ni0,1≤i≤n0,we used the least square and usual non parametric methods to define the estimates ^β_n and g_n for β and g and obtained their r-th mean consistency or strong consistency under suiteble conditions.
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Considering semiparametric regression model with a liner process enors Y_(ni0=(β·t_(ni0+g(x_(ni0)+e_(ni0,1≤i≤n0,we used the least square and usual non parametric methods to define the estimates ^β_n and g_n for β and g and obtained their r-th mean consistency or strong consistency under suiteble conditions.
Key concepts: Consistency (knowledge bases), Semiparametric regression, Semiparametric model, Mathematics, Linear regression, Regression, Parametric statistics, Statistics