hdMTD_FSC {hdMTD} | R Documentation |
Forward Stepwise and Cut method for inference in MTD models
Description
A function for inference in MTD Markov chains with FSC method. This function estimates the relevant
lag set \Lambda
of an MTD model through the FSC algorithm.
Usage
hdMTD_FSC(X, d, l, alpha = 0.05, mu = 1, xi = 0.5, A = NULL, ...)
Arguments
X |
A vector or single-column data frame containing a chain sample ( |
d |
A positive integer representing an upper bound for the chain order. |
l |
A positive integer that sets the number of elements in the output vector. |
alpha |
A positive real number used in the CUT threshold (which determines if two
distributions can be considered different). The larger the |
mu |
A positive real number such that |
xi |
A positive real number, |
A |
A vector with positive integers representing the state space. If not informed,
this function will set |
... |
Additional arguments (not used in this function, but maintained for compatibility with |
Details
The "Forward Stepwise and Cut" (FSC) is an algorithm for inference in Mixture Transition Distribution (MTD) models. It consists in the application of the "Forward Stepwise" (FS) step followed by the CUT algorithm. This method and its steps where developed by Ost and Takahashi and are specially useful for inference in high-order MTD Markov chains.
Value
Returns a vector with the estimated relevant lag set using FSC algorithm.
References
Ost, G. & Takahashi, D. Y. (2023). Sparse Markov models for high-dimensional inference. Journal of Machine Learning Research, 24(279), 1-54. http://jmlr.org/papers/v24/22-0266.html
Examples
X <- testChains[,1]
hdMTD_FSC(X,4,3,alpha=0.02)
hdMTD_FSC(X,4,2,alpha=0.001)