The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Fredrik Dahl - One of the best experts on this subject based on the ideXlab platform.
-
Combining bootstrap-based stroke incidence Models with discrete event Modeling of travel-time and stroke treatment: Non-normal input and non-linear output
2017 Winter Simulation Conference (WSC), 2017Co-Authors: Kim Rand-hendriksen, Joe Viana, Fredrik DahlAbstract:Incidence rates in simulation Models are often assumed to stem from Poisson processes, with rates based on analyses of real-life data. In cases where the record of data is limited, or observed rates are low, the stochastic process involved in sampling from Modeled distributions may not adequately reflect the uncertainty around the estimated input parameters. We present a conceptually simple, but computationally demanding, method for generating variance in incidence through the use of bootstrapping; for each subsample, a regression Model is Fitted, and the simulation Model is run repeatedly sampling from the Fitted Model. Stochasticity is introduced at two levels; data for fitting the regression, and sampling from the Fitted Model. We illustrate this hybrid approach using Norwegian stroke records to generate stroke incidences with age, sex, and location, in a simulation Model made to analyze travel time, queuing, and time to treatment in regional stroke units.
Kim Rand-hendriksen - One of the best experts on this subject based on the ideXlab platform.
-
WSC - Combining bootstrap-based stroke incidence Models with discrete event Modeling of travel-time and stroke treatment: non-normal input and non-linear output
2017 Winter Simulation Conference (WSC), 2017Co-Authors: Kim Rand-hendriksen, Joe Viana, Fredrik A. DahlAbstract:Incidence rates in simulation Models are often assumed to stem from Poisson processes, with rates based on analyses of real-life data. In cases where the record of data is limited, or observed rates are low, the stochastic process involved in sampling from Modeled distributions may not adequately reflect the uncertainty around the estimated input parameters. We present a conceptually simple, but computationally demanding, method for generating variance in incidence through the use of bootstrapping; for each subsample, a regression Model is Fitted, and the simulation Model is run repeatedly sampling from the Fitted Model. Stochasticity is introduced at two levels; data for fitting the regression, and sampling from the Fitted Model. We illustrate this hybrid approach using Norwegian stroke records to generate stroke incidences with age, sex, and location, in a simulation Model made to analyze travel time, queuing, and time to treatment in regional stroke units.
-
Combining bootstrap-based stroke incidence Models with discrete event Modeling of travel-time and stroke treatment: Non-normal input and non-linear output
2017 Winter Simulation Conference (WSC), 2017Co-Authors: Kim Rand-hendriksen, Joe Viana, Fredrik DahlAbstract:Incidence rates in simulation Models are often assumed to stem from Poisson processes, with rates based on analyses of real-life data. In cases where the record of data is limited, or observed rates are low, the stochastic process involved in sampling from Modeled distributions may not adequately reflect the uncertainty around the estimated input parameters. We present a conceptually simple, but computationally demanding, method for generating variance in incidence through the use of bootstrapping; for each subsample, a regression Model is Fitted, and the simulation Model is run repeatedly sampling from the Fitted Model. Stochasticity is introduced at two levels; data for fitting the regression, and sampling from the Fitted Model. We illustrate this hybrid approach using Norwegian stroke records to generate stroke incidences with age, sex, and location, in a simulation Model made to analyze travel time, queuing, and time to treatment in regional stroke units.
J.k. Tugnait - One of the best experts on this subject based on the ideXlab platform.
-
Closed loop linear Model validation and order estimation using polyspectral analysis
Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 1999Co-Authors: Yi Zhou, J.k. TugnaitAbstract:Previously, various techniques have been proposed for closed loop system identification using polyspectral analysis. Having obtained a Model, how do we know if the Fitted Model is "good?" This paper is devoted to the problem of Model validation using polyspectral analysis. We propose simple statistical tests based upon the estimated polyspectrum (integrated bispectrum and/or integrated trispectrum) of an output error signal or the estimated cross-polyspectrum between the external reference and the output error signal. Model order estimation is a by-product of the Model validation procedure. Illustrative computer simulation examples are presented.
-
Model diagnostics and validation for linear Model fitting using higher-order statistics
1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: J.k. TugnaitAbstract:Given a linear stationary non-Gaussian signal, suppose that we fit a linear Model using higher-order statistics and one of several existing methods. The Model is Fitted under certain assumptions on the data and the underlying (true) Model. Having obtained a Model, how do we know if the Fitted Model is "good"? This paper is devoted to the problem of Model diagnostics and validation. We propose some simple frequency-domain tests that are applicable to both third-order and fourth-order statistics-based Model fitting unlike existing tests. A computer simulation example is presented to illustrate the proposed tests.
Joe Viana - One of the best experts on this subject based on the ideXlab platform.
-
WSC - Combining bootstrap-based stroke incidence Models with discrete event Modeling of travel-time and stroke treatment: non-normal input and non-linear output
2017 Winter Simulation Conference (WSC), 2017Co-Authors: Kim Rand-hendriksen, Joe Viana, Fredrik A. DahlAbstract:Incidence rates in simulation Models are often assumed to stem from Poisson processes, with rates based on analyses of real-life data. In cases where the record of data is limited, or observed rates are low, the stochastic process involved in sampling from Modeled distributions may not adequately reflect the uncertainty around the estimated input parameters. We present a conceptually simple, but computationally demanding, method for generating variance in incidence through the use of bootstrapping; for each subsample, a regression Model is Fitted, and the simulation Model is run repeatedly sampling from the Fitted Model. Stochasticity is introduced at two levels; data for fitting the regression, and sampling from the Fitted Model. We illustrate this hybrid approach using Norwegian stroke records to generate stroke incidences with age, sex, and location, in a simulation Model made to analyze travel time, queuing, and time to treatment in regional stroke units.
-
Combining bootstrap-based stroke incidence Models with discrete event Modeling of travel-time and stroke treatment: Non-normal input and non-linear output
2017 Winter Simulation Conference (WSC), 2017Co-Authors: Kim Rand-hendriksen, Joe Viana, Fredrik DahlAbstract:Incidence rates in simulation Models are often assumed to stem from Poisson processes, with rates based on analyses of real-life data. In cases where the record of data is limited, or observed rates are low, the stochastic process involved in sampling from Modeled distributions may not adequately reflect the uncertainty around the estimated input parameters. We present a conceptually simple, but computationally demanding, method for generating variance in incidence through the use of bootstrapping; for each subsample, a regression Model is Fitted, and the simulation Model is run repeatedly sampling from the Fitted Model. Stochasticity is introduced at two levels; data for fitting the regression, and sampling from the Fitted Model. We illustrate this hybrid approach using Norwegian stroke records to generate stroke incidences with age, sex, and location, in a simulation Model made to analyze travel time, queuing, and time to treatment in regional stroke units.
Afshin Ahmadi - One of the best experts on this subject based on the ideXlab platform.
-
a three dimensional non hydrostatic vertical boundary Fitted Model for free surface flows
International Journal for Numerical Methods in Fluids, 2008Co-Authors: Peyman Badiei, Masoud Montazeri Namin, Afshin AhmadiAbstract:A non-hydrostatic finite volume Model is presented to simulate three-dimensional (3D) free-surface flows on a vertical boundary Fitted grid system. The algorithm, which is an extension to the previous two dimensional vertical (2DV) Model proposed by Ahmadi et al. (Int. J. Numer. Meth. Fluids 2007; 54(9):1055-1074), solves the complete 3D Navier-Stokes equations in two major steps based on projection method. First, by excluding the pressure terms in momentum equations, a set of advection-diffusion equations are obtained. In the second step, the continuity and the momentum equations with the remaining pressure terms are solved which yields a block tri-diagonal system of equations with pressure as the unknown. In this step, the 3D system is decomposed into a series of 2DV plane sub-systems which are solved individually by a direct matrix solver. Iteration is required to ensure convergence of global 3D system. To minimize the number of vertical layers and subsequently the computational cost, a new top-layer pressure treatment is proposed which enables the Model to simulate a range of surface waves using only 2-5 vertical layers.
-
A three‐dimensional non‐hydrostatic vertical boundary Fitted Model for free‐surface flows
International Journal for Numerical Methods in Fluids, 2008Co-Authors: Peyman Badiei, Masoud Montazeri Namin, Afshin AhmadiAbstract:A non-hydrostatic finite volume Model is presented to simulate three-dimensional (3D) free-surface flows on a vertical boundary Fitted grid system. The algorithm, which is an extension to the previous two dimensional vertical (2DV) Model proposed by Ahmadi et al. (Int. J. Numer. Meth. Fluids 2007; 54(9):1055-1074), solves the complete 3D Navier-Stokes equations in two major steps based on projection method. First, by excluding the pressure terms in momentum equations, a set of advection-diffusion equations are obtained. In the second step, the continuity and the momentum equations with the remaining pressure terms are solved which yields a block tri-diagonal system of equations with pressure as the unknown. In this step, the 3D system is decomposed into a series of 2DV plane sub-systems which are solved individually by a direct matrix solver. Iteration is required to ensure convergence of global 3D system. To minimize the number of vertical layers and subsequently the computational cost, a new top-layer pressure treatment is proposed which enables the Model to simulate a range of surface waves using only 2-5 vertical layers.