This paper develops estimation and inference methods for conditional quantile factor models. We first introduce a simple sieve estimation, establish asymptotic properties of the estimators under large $N$. then provide bootstrap procedure estimating distributions estimators. also two consistent number factors. The allow us not only to estimate structures asset returns utilizing characteristics,...

The single-index model is a statistical for intrinsic regression where responses are assumed to depend on single yet unknown linear combination of the predictors, allowing express function as E[Y|X]=f(⟨v,X⟩) some index vector v and link f. Conditional methods provide simple effective approach estimate by averaging moments X conditioned Y, but parameters whose optimal choice do not generalizatio...

A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of variables and their probabilistic independencies. Some researches often involve continuous random variables. In order to apply these continuous variables to BN models, these variables should convert into discrete variables with limited states, often two. During the discretization process, one pr...

We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum e...