Following the work of Hurvich, Shumway, and Tsai (1990), we propose an “improved” variant of the Akaike information criterion, AICi, for state-space model selection. The variant is based on Akaike’s (1973) objective of estimating the Kullback-Leibler information (Kullback 1968) between the densities corresponding to the fitted model and the generating or true model. The development of AICi proc...

We derive the proper form of the Akaike information criterion for variable selection for mixture cure models, which are often fit via the expectation-maximization algorithm. Separate covariate sets may be used in the mixture components. The selection criteria are applicable to survival models for right-censored data with multiple competing risks and allow for the presence of an insusceptible gr...

background : gastric cancer is the one of the most prevalent reason of cancer-related death in the world. survival of patients after surgery involves identifying risk factors. there are various models to detect the effect of risk factors on patients’ survival. the present study aims at evaluating these models. methods : data from 330 gastric cancer patients diagnosed at the iran cancer institut...

A two component parametric mixture is proposed to model survival after an invasive treatment, when patients may experience different hazards regimes: a risk of early mortality directly related to the treatment and/or the treated condition, and a risk of late death influenced by several exogenous factors. The parametric mixture is based on Weibull distributions for both components. Different set...

The Likelihood Principle has been defended on Bayesian grounds, on the grounds that it coincides with and systematizes intuitive judgments about example problems, and by appeal to the fact that it generalizes what is true when hypotheses have deductive consequences about observations. Here we divide the Principle into two parts -one qualitative, the other quantitative -and evaluate each in the ...

Keywords: Bayesian model selection Reversible jump Markov chain Monte Carlo Autoregressive fractional integrated moving average models Long memory processes a b s t r a c t Various model selection criteria such as Akaike information criterion (AIC; Akaike, 1973), Bayesian information criterion (BIC; Akaike, 1979) and Hannan–Quinn criterion (HQC; Hannan, 1980) are used for model specification in...

本報は，葛葉・水木（2021a）（前報）を補足するものである．前報では，T年確率水文量の算定手順に関し，修正SLSC法を用いた手法を提示した．しかし，その手法では，そこで問題にした「2重の規準の問題」は解決できない．通常，我々は，A-規準（L-moment，対数尤度など）によって母数を推定し，B-規準（SLSC，AICなど）によって確率分布間の優劣を評価して最適な確率分布を選定する．しかし，A-規準とB-規準が異なるのは問題である．これを解決するために，最尤法で母数を推定してAICなどの情報量による規準値で確率分布間の比較をすればよいことは，最尤法とAIC等の情報量規準の関係ゆえ，おそらく多くの研究者が気づいていると考える．