yunhui, fu matsushima, shin yamanishi, kenji

insights or trial and error. This paper proposes a novel rank selection criterion for NTF on the basis of the minimum description length (MDL) principle. Our methodology is unique in that (1) we apply the MDL principle on tensor slices to overcome a problem caused by the imbalance between the number of elements in a data tensor and that in factor m...

xiao, hui sun, yiguo

Model selection and model averaging are popular approaches for handling modeling uncertainties. The existing literature offers a unified framework for variable selection via penalized likelihood and the tuning parameter selection is vital for consistent selection and optimal estimation. Few studies have explored the finite sample performances of th...

román-román, patricia serrano-pérez, juan josé torres-ruiz, francisco

The behaviour of many dynamic real phenomena shows different phases, with each one following a sigmoidal type pattern. This requires studying sigmoidal curves with more than one inflection point. In this work, a diffusion process is introduced whose mean function is a curve of this type, concretely a transformation of the well-known Gompertz model ...

xiang, ning landschoot, christopher

This work applies two levels of inference within a Bayesian framework to accomplish estimation of the directions of arrivals (DoAs) of sound sources. The sensing modality is a spherical microphone array based on spherical harmonics beamforming. When estimating the DoA, the acoustic signals may potentially contain one or multiple simultaneous source...

weiß, christian h. m. feld, martin h.-j. khan, naushad mamode sunecher, yuvraj

While most of the literature about INARMA models (integer-valued autoregressive moving-average) concentrates on the purely autoregressive INAR models, we consider INARMA models that also include a moving-average part. We study moment properties and show how to efficiently implement maximum likelihood estimation. We analyze the estimation performanc...

pérez-sánchez, julio senent-aparicio, javier segura-méndez, francisco pulido-velazquez, david srinivasan, raghavan

Sutcliffe model efficiency coefficient (NSE), coefficient of determination (R2), percent bias (PBIAS), and the relative error between observed and simulated run-off volumes (REV). Furthermore, we applied the FITEVAL software to determine the uncertainty of the model. The results show that when the catchments are more humid the obtained results are ...

oliveira, josé manuel ramos, patrícia

namely, state space models and ARIMA. Appropriate models from both families are chosen for each time-series by minimising the bias-corrected Akaike information criteria. The results show significant improvements in forecast accuracy, providing valuable information to support management decisions. It is clear that reconciled forecasts using the Mini...

yang, hou-cheng guanyu, hu chen, ming-hui

Generalized linear models are routinely used in many environment statistics problems such as earthquake magnitudes prediction. Hu et al. proposed Pareto regression with spatial random effects for earthquake magnitudes. In this paper, we propose Bayesian spatial variable selection for Pareto regression based on Bradley et al. and Hu et al. to tackle...

Comte, Fabienne Genon-Catalot, Valentine

We consider N independent stochastic processes (Xi(t), t ∈ [0, T ]), i = 1,. .. , N , dened by a one-dimensional stochastic dierential equation which are continuously observed throughout a time interval [0, T ] where T is xed. We study nonparametric estimation of the drift function on a given subset A of R. Projection estimators are dened on nite d...

Comte, Fabienne Genon-Catalot, Valentine

We consider N independent stochastic processes (Xi(t), t ∈ [0, T ]), i = 1,. .. , N , dened by a one-dimensional stochastic dierential equation which are continuously observed throughout a time interval [0, T ] where T is xed. We study nonparametric estimation of the drift function on a given subset A of R. Projection estimators are dened on nite d...