Subscribe Now Subscribe Today
Science Alert
Curve Top
Journal of Applied Sciences
  Year: 2007 | Volume: 7 | Issue: 6 | Page No.: 922-925
DOI: 10.3923/jas.2007.922.925
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Reconstruction of Time Series Data with Missing Values

Mitat Uysal

Missing data are a part of almost all research and it must be decided how to deal with it from time to time. Missing data creates several problems in many applications which depend on good access to accurated data. Conventional methods for missing data, like listwise deletion or regression imputation, are prone to three serious problems: Inefficient use of the available information, leading to low power and Type II errors. Biased estimates of standard errors, leading to incorrect p-values. Biased parameter estimates, due to failure to adjust for selectivity in missing data. In this study, we propose a new algorithm to predict missing values of a given time series using Radial Basis Functions.
PDF Fulltext XML References Citation Report Citation
  •    A Comparison of Methods to Detect Publication Bias for Meta-analysis of Continuous Data
  •    Pattern Mixture Modeling: An Application in Anti Diabetes Drug Therapy on Serum Creatinine in Type 2 Diabetes Patients
How to cite this article:

Mitat Uysal , 2007. Reconstruction of Time Series Data with Missing Values. Journal of Applied Sciences, 7: 922-925.

DOI: 10.3923/jas.2007.922.925






Curve Bottom