Paper Info
Title
Point and Interval Estimation Method for Auto-regressive Model with Nonnormal Error
Abstract
Estimation in time series analysis aids in making a reasonable decision by providing a value for point estimation and a range of interval estimation. An auto-regressive model is designed for the time series analysis. However, the auto-regressive model may cause decreasing accuracy and prediction in estimating parameters because it uses the assumption that the distribution of error term follows a normal distribution. In reality, there are plenty of data indicating that the distribution of error term does not follow the normal distribution. Thus, we propose a method for solving this problem by using a Pearson distribution system and maximum likelihood estimation. Compared with existing methods, the proposed method can be applied to various time series data requiring high accuracy and prediction.
Year
DOI
Venue
2013
10.1109/BigData.Congress.2013.57
BigData Congress
Keywords
Field
DocType
normal distribution,time series analysis,interval estimation,point estimation,various time series data,auto-regressive model,nonnormal error,point estimation method,parameter estimation,maximum likelihood estimation,pearson distribution system,error term distribution,time series analysis aid,interval estimation method,autoregressive processes,nonnormal data,error term,autoregressive model,time series
Maximum spacing estimation,Interval estimation,Normal distribution,Computer science,Minimum chi-square estimation,Distribution fitting,Half-normal distribution,Three-point estimation,Statistics,Generalized normal distribution
Conference
ISSN
ISBN
Citations 
2379-7703
978-0-7695-5006-0
0
PageRank 
References 
Authors
0.34
1
4
Name
Order
Citations
PageRank
Bo-mi Lim1616.97
Jongwoo Kim224228.35
Sung-Shick Kim3626.44
jungeol baek47411.95