We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references ...

The problem of quickest detection of a change in distribution is considered under the assumption that the pre-change distribution is known, and the post-change distribution is only known to belong to a family of distributions distinguishable from a discretized version of the pre-change distribution. A sequential change detection procedure is proposed that partitions the sample space into a fini...

The non-normality of financial asset returns has important implications for hedging. In particular, in contrast with the unambiguous effect that minimum-variance hedging has on the standard deviation, it can actually increase the negative skewness and kurtosis of hedge portfolio returns. Thus the reduction in Value at Risk (VaR) and Conditional Value at Risk (CVaR) that minimum-variance hedging...

Limiting tail behavior of distributions are known to follow one of three possible limiting distributions, depending on the domain of attraction of the observational model under suitable regularity conditions. This work proposes a new approach for identification and analysis of the limiting regimes that these data exceedances belong to. The model-based approach uses a mixture at the observationa...

An accurate estimate of the uncertainty associated with a parameter estimate is important if we want to avoid misleading inference. The bootstrap technique (Efron, 1979; Efron and Tisbshirani, 1993) is a very general way of measuring the accuracy of estimators, and was originally developed for parameter estimation given independent identically distributed (i.i.d.) data. However, random effects ...

Majority of time series clustering research is focused on calculating similarity metrics between individual series, which in conjunction with traditional clustering algorithm partitions the data into similar groups (clusters). A major challenge lies in obtaining partitions when the number of clusters is not known in advance. Another challenge in such a clustering problem is to apply known hiera...

Riassunto: Il lavoro riguarda il processo di convergenza dei paesi appartenenti al partenariato Euro-Mediterraneo nel periodo 1980-1999. Per la misura della convergenza viene proposto un indicatore ed una matrice di “persistenza” che rappresentano, in modo sintetico, il risultato di un algoritmo di classificazione. L’informazione fornita dalla matrice di persistenza viene ridotta successivament...

Bioequivalence assessment is an issue of great interest. Development of statistical methods for assessing bioequivalence is an important area of research for statisticians. Bioequivalence is usually determined based on the normal distribution. We relax this assumption and develop a semi-parametric mixed model for bioequivalence data. The proposed method is quite flexible and practically meaning...

The linear mixed effects model with normal errors is a popular model for the analysis of repeated measures and longitudinal data. The generalized linear model is useful for data that have non-normal errors but where the errors are uncorrelated. A descendant of these two models generates a model for correlated data with non-normal errors, called the generalized linear mixed model (GLMM). Frequen...