STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL
Statistical dependence exploration of climate data using copula, Markov mo- del, and hidden Markov model will be discussed in this thesis . Many researches on climatology usually use assumptions on the variables being normally distri- buted or having the same marginal distribution function. In re...
Saved in:
| Main Author: | |
|---|---|
| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/44474 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Statistical dependence exploration of climate data using copula, Markov mo-
del, and hidden Markov model will be discussed in this thesis . Many researches
on climatology usually use assumptions on the variables being normally distri-
buted or having the same marginal distribution function. In reality, only some
variables are normally distributed. In addition, each variable would not have
same marginal distribution function. These assumptions cause the analysis
result to be far from reality. As an alternative the problem can be solved by
copula method. In this thesis, copula method will be applied to study the
dependence to sequence time series data. Transition probability of one state
to other state in Markov model will then be determined and used to estimate
next state using hidden Markov model. |
|---|