MTAFT:Data-Driven Estimation for Multi-Threshold Accelerate Failure
Time Model
Developed a data-driven estimation framework for the multi-threshold accelerate failure time (MTAFT) model. The
MTAFT model features different linear forms in different
subdomains, and one of the major challenges is determining the
number of threshold effects. The package introduces a
data-driven approach that utilizes a Schwarz' information
criterion, which demonstrates consistency under mild
conditions. Additionally, a cross-validation (CV) criterion
with an order-preserved sample-splitting scheme is proposed to
achieve consistent estimation, without the need for additional
parameters. The package establishes the asymptotic properties
of the parameter estimates and includes an efficient score-type
test to examine the existence of threshold effects. The
methodologies are supported by numerical experiments and
theoretical results, showcasing their reliable performance in
finite-sample cases.
