Search results for "multivariate statistic"

Kernel intensity for space-time point processes with application to seismological problems

2010

Dealing with data coming from a space-time inhomogeneous process, there is often the need of semi-parametric estimates of the conditional intensity function; isotropic or anisotropic multivariate kernel estimates can be used, with windows sizes h. The properties of the intensities estimated with this choice of h are not always good for specific fields of application; we could try to choose h in order to have good predictive properties of the estimated intensity function. Since a direct ML approach cannot be followed, we propose an estimation procedure, computationally intensive, based on the subsequent increments of likelihood obtained adding an observation at time. The first results obtain…

Stochastic Nonlinear Time Series Forecasting Using Time-Delay Reservoir Computers: Performance and Universality

2014

International audience; Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay diFFerential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We …

Forecasting correlated time series with exponential smoothing models

2011

Abstract This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters’ model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection crite…

Multivariate stochastic wave generation

1996

Abstract In this paper, for the case of the fluid particle velocity, a procedure that substantially reduces the computational effort to generate a multivariate stochastic process is proposed. It is shown that, for a fully coherent wave field, it is possible to decompose the Power Spectral Density (PSD) matrix into the eigenvectors of the matrix itself. This leads to generate each field's process as independent, and the time generation increases linearly with the processes' number in the field. A numerical example to evaluate the statistical properties, in terms of correlation and cross-correlation functions, of the processes is also presented.

Multivariate regression analysis applied to the calibration of equipment used in pig meat classification in Romania.

2016

This paper highlights the statistical methodology used in a dissection experiment carried out in Romania to calibrate and standardize two classification devices, OptiGrade PRO (OGP) and Fat-o-Meat'er (FOM). One hundred forty-five carcasses were measured using the two probes and dissected according to the European reference method. To derive prediction formulas for each device, multiple linear regression analysis was performed on the relationship between the reference lean meat percentage and the back fat and muscle thicknesses, using the ordinary least squares technique. The root mean squared error of prediction calculated using the leave-one-out cross validation met European Commission (EC…

So Many Variables: Joint Modeling in Community Ecology

2015

Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by exa…

Option-Implied Volatility-Managed Asset Pricing Risk Factors and Resurrection of the Value Factor

2019

Option-implied volatility-managed risk factor models produce higher maximum squared Sharpe ratios than the recently proposed six-factor model, which is used as a benchmark model in this study. A model that incorporates option-implied volatility-managed risk factors based on dynamic scaling factors that systematically overestimate the expected market risk, as measured by the VIX, is superior to other asset pricing model specifications. After the death of the value factor has been repeatedly declared, it is surprising news that multivariate spanning regressions reveal that both the option-implied volatility-managed momentum and value factor are the only option-implied volatility-managed risk …

2019

Objective A short questionnaire which can be applied for assessing patient satisfaction in different contexts and different countries is to be developed. Methods Six items addressing tangibles, reliability, responsiveness, assurance, empathy, and communication were analysed. The first five items stem from SERVQUAL (SERVice QUALity), the last stems from the discussion about SERVQUAL. The analyses were performed with data from 12 surveys conducted in six different countries (England, Finland, Germany, Greece, the Netherlands, Spain) covering two different conditions (type 2 diabetes, stroke). Sample sizes for included participants are 247 in England, 160 in Finland, 231 in Germany, 152 in Gre…

Information decomposition in the frequency domain: a new framework to study cardiovascular and cardiorespiratory oscillations

2021

While cross-spectral and information-theoretic approaches are widely used for the multivariate analysis of physiological time series, their combined utilization is far less developed in the literature. This study introduces a framework for the spectral decomposition of multivariate information measures, which provides frequency-specific quantifications of the information shared between a target and two source time series and of its expansion into amounts related to how the sources contribute to the target dynamics with unique, redundant and synergistic information. The framework is illustrated in simulations of linearly interacting stochastic processes, showing how it allows us to retrieve …

Advanced Statistical Techniques

2012