Non-parametric and Flexible Time Series Estimators

2014 ◽  
pp. 339-356
Author(s):  
Jürgen Franke ◽  
Wolfgang Karl Härdle ◽  
Christian Matthias Hafner
Keyword(s):  
Time Series ◽  
Non Parametric

A non-parametric model for fuzzy forecasting time series data

2021 ◽  
Vol 40 (4) ◽  
Author(s):  
Gholamreza Hesamian ◽  
Mohammad Ghasem Akbari
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Parametric Model ◽  
Series Data ◽  
Non Parametric

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

Sign in / Sign up

Export Citation Format

Share Document