We consider random-design nonparametric regression model in which errors depend on predictors as well as on unobservable latent variables. Predictors and latent variables may be shortor long-range dependent. In this setup asymptotic distributions of the Nadaraya-Watson estimate of regression function are studied under various conditions. We prove that their form depends on three factors: amount...

It has often been argued that all techniques of standard setting are arbitrary and likely to yield different results for different techniques or persons. This paper deals with a related but hitherto ignored aspect of standard setting, namely the possibility that Angoff or Nedelsky judges misspecify the probabilities of the borderline student's success on the items because they do not use the ps...

The problem of understanding people’s participation in realworld events has been a subject of active research and can offer valuable insights for human behavior analysis and event-related recommendation/advertisement. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in three major cities around the w...

We present an approach for pricing of illiquid bonds (and bond derivatives) in an arbitrage-free way and consistently with observed prices of liquid bonds. The basic model is a multifactor term structure model with abstract latent factors. The approach is based on stochastic ltering techniques, leading to a continuous update of the distribution of the latent factors on the basis of the informat...

In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty. It is based on a simple idea of disentangling components of the future state which are predictable from those which are inherently unpredictable, and encoding the unpredictable components into a low-dimensional latent variable which is fed into a forward model. Our method uses a supervi...

We present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was made earlier [6], but that study involved only small applications with 4 or 5 observed variables. Due to recent progress in algorithm research, it is now possible to learn HLC models with dozens of observed variables. ...

Among latent variables that can be used in e-collaboration research, job performance is a particularly important one. It measures what most e-collaboration tools in organizations aim to improve, namely the performance at work of individuals executing tasks collaboratively with others. We report on a comparative assessment of scores generated based on a self-reported job performance measurement ...