hdMTD_CUT {hdMTD} | R Documentation |
The CUT method for inference in MTD models
Description
A function that estimates the set of relevant lags of an MTD model using the CUT method.
Usage
hdMTD_CUT(
X,
d,
S = 1:d,
alpha = 0.05,
mu = 1,
xi = 0.5,
A = NULL,
warning = FALSE,
...
)
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. |
S |
A numeric vector of distinct positive integers from which this function will select
a set of relevant lags. Should be a subset of |
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 |
warning |
Logical. If |
... |
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. This specific function will only apply the CUT step of the algorithm and return an estimated relevant lag set.
Value
Returns a set of relevant lags estimated using the CUT 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[,3]
hdMTD_CUT(X,4,alpha=0.02,mu=1,xi=0.4)
hdMTD_CUT(X,d=6,S=c(1,4,6),alpha=0.0065)