Optimal Choice of Parameters for Unknown Function in Big Data Analysis Problems

被引：0
|
作者：
Hajiyev, Asaf [1 ]
Narmina, Abdullayeva [2 ]
机构：
[1] Azerbaijan Natl Acad Sci, Inst Syst Control, Baku, Azerbaijan
[2] Azerbaijan Turizm Univ, Baku, Azerbaijan
来源：
PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT | 2015年 / 362卷
关键词：
Regression models; Increasing number of parameters; Least square estimators; Confidence band; Eigen-value; INCREASING NUMBERS; MODELS;
D O I：
10.1007/978-3-662-47241-5_34
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
New form of regression models with increasing number of unknown parameters and unknown and different values of errors variance are considered. It is supposed that a number of unknown parameters can increase when number of observations becomes large. At the each point there is only one observation, which does not allow to estimate variances. The new method for estimating of unknown parameters, choice of an optimal number of unknown parameters and construction a confident band for unknown function in these models is suggested. Numerical examples, demonstrating these results are given.
引用
收藏
页码：399 / 408
页数：10
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