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Kenneth D West - One of the best experts on this subject based on the ideXlab platform.
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forecast evaluation of small Nested Model sets
Journal of Applied Econometrics, 2010Co-Authors: Kirstin Hubrich, Kenneth D WestAbstract:SUMMARY We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark Model to the MSPEs of a small set of alternative Models that nest the benchmark. Our procedures compare the benchmark to all the alternative Models simultaneously rather than sequentially, and do not require reestimation of Models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, while the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have the most accurate size, and the procedure that looks at the maximum t-statistic has the best power. We illustrate our procedures by comparing forecasts of different Models for US inflation. Copyright 2010 John Wiley & Sons, Ltd.
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forecast evaluation of small Nested Model sets
Research Papers in Economics, 2009Co-Authors: Kirstin Hubrich, Kenneth D WestAbstract:We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark Model to the MSPEs of a small set of alternative Models that nest the benchmark. Our procedures compare the bench-mark to all the alternative Models simultaneously rather than sequentially, and do not require re-estimation of Models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different Models for U.S. inflation. JEL Classification: C32, C53, E37
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forecast evaluation of small Nested Model sets
Social Science Research Network, 2008Co-Authors: Kirstin Hubrich, Kenneth D WestAbstract:We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark Model to the MSPEs of a small set of alternative Models that nest the benchmark. Our procedures compare the benchmark to all the alternative Models simultaneously rather than sequentially, and do not require reestimation of Models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White's (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different Models for U.S. inflation.
James R. Ewing - One of the best experts on this subject based on the ideXlab platform.
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SU-F-I-26: Maximum Likelihood and Nested Model Selection Techniques for Pharmacokinetic Analysis of Dynamic Contrast Enhanced MRI in Patients with Glioblastoma Tumors
Medical Physics, 2016Co-Authors: Hassan Bagher-ebadian, Azimeh N.v. Dehkordi, James R. EwingAbstract:Purpose: This pilot study introduces a novel approach for estimation of pharmacokinetic parameters in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). In this study Maximum Likelihood (ML) and Nested Model Selection (NMS) techniques are combined to construct an approximately unbiased estimator for DCE-MRI data analysis. Methods: DCE T1-weighted MRI using the contrast agent (CA) gadopentetate dimeglumine was performed on 20 patients with Glioblastoma tumor. ML Estimation (MLE) technique was recruited for optimizing 3 physiologically Nested Models constructed based on the extended Tofts Model in the course of DCE-MRI experimental. The Log-Likelihood-Ratio (LLR) measures for three Nested Models were used to choose the best Model explaining the variation of the experimental DCE-MRI data and to estimate its Pharmacokinetic (PK) parameters. The observed information matrix or the matrix of Log-Likelihood was used to estimate the variance and co-variance of the estimated PK parameters for each of selected Models. Results: The PK parameters along with the Model choice maps estimated by the MLE and NMS are highly in agreement with the physiological condition of underlying pathology and the values for the permeability parameters of the brain reported by the literature. The low variance and covariance measures of the estimated PK parameters being reasonably in-range imply that the proposed estimator is robust for estimation of physiological parameters in DCE-MRI studies. Conclusion: This pilot study confirms that only three parameters of the standard Model are sufficient to fit the most complicated time trace of CA concentration in DCE-T1 weighted studies for GBM tumors under the conditions of the experiment. This study is supported in part by Dykstra Family (F60570) and mentored grants (A10237)
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Measurement of rat brain tumor kinetics using an intravascular MR contrast agent and DCE-MRI Nested Model selection
Journal of Magnetic Resonance Imaging, 2014Co-Authors: Wilson B Chwang, Hassan Bagher-ebadian, Rajan Jain, A S M Iskander, Ashley Vanslooten, Lonni Schultz, Siamak P. Nejad-davarani, Ali S. Arbab, James R. EwingAbstract:Dynamic contrast-enhanced T1-weighted magnetic resonance imaging (DCE-MRI) is being increasingly used in various clinical trials involving brain tumors. It allows characterization of the vascular microenvironment in tumors by measurement of a range of parameters, such as Ktrans (forward transfer constant), kep (reverse transfer constant), ve (volume of the extravascular extracellular space), and vp (blood plasma volume) (1,2). These parameters reflect specific physiologic characteristics and hence relate to various aspects of tumor biology. One of the hurdles in obtaining these quantitative metrics is a lack of robust methods for approaching the problem of parametric estimation using multicompartmental pharmacokinetic Models (3). Another issue is that current Food and Drug Administration (FDA)-approved extravascular contrast agents (CAs) used for the assessment of vascular parameters in solid tumors are relatively small (molecular weight
Luc Vandenbulcke - One of the best experts on this subject based on the ideXlab platform.
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multigrid state vector for data assimilation in a two way Nested Model of the ligurian sea
Journal of Marine Systems, 2007Co-Authors: Alexander Barth, Aida Alveraazcarate, J M Beckers, M Rixen, Luc VandenbulckeAbstract:Abstract A system of two Nested Models composed by a coarse resolution Model of the Mediterranean Sea, an intermediate resolution Model of the Provencal Basin and a high resolution Model of the Ligurian Sea is coupled with a Kalman-filter based assimilation method. The state vector for the data assimilation is composed by the temperature, salinity and elevation of the three Models. The forecast error is estimated by an ensemble run of 200 members by perturbing initial condition and atmospheric forcings. The 50 dominant empirical orthogonal functions (EOF) are taken as the error covariance of the Model forecast. This error covariance is assumed to be constant in time. Sea surface temperature (SST) and sea surface height (SSH) are assimilated in this system.
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local assimilation of sea surface temperature and elevation in a two way Nested Model of the gulf of lions using a single multigrid state vector
2005Co-Authors: Luc Vandenbulcke, Alexander Barth, Ben Z Bouallegue, Jeanmarie BeckersAbstract:A three fold Nested Model is built, covering (a) the Mediterranean Sea (resolution 1/4 degree) (b) its North-Western part (resolution 1/20 degree), and (c) the Gulf of Lions (resolution 1/100 degree). The GHER hydrodynamic Model (see e.g. [1]) is used for a simulation of the springs of 1997 and 1998. As the Model allows mode splitting, the timestep in each grid is 3 seconds for the barotropic modes, and 3 minutes for the baroclinic modes. ECMWF atmospheric forcings and MODB4/MEDAR climatic data are used. This simulation is run with one-directionnal and bi-directionnal nesting (i.e. without and with statevector feedback), and results are compared.
Kirstin Hubrich - One of the best experts on this subject based on the ideXlab platform.
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forecast evaluation of small Nested Model sets
Journal of Applied Econometrics, 2010Co-Authors: Kirstin Hubrich, Kenneth D WestAbstract:SUMMARY We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark Model to the MSPEs of a small set of alternative Models that nest the benchmark. Our procedures compare the benchmark to all the alternative Models simultaneously rather than sequentially, and do not require reestimation of Models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, while the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have the most accurate size, and the procedure that looks at the maximum t-statistic has the best power. We illustrate our procedures by comparing forecasts of different Models for US inflation. Copyright 2010 John Wiley & Sons, Ltd.
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forecast evaluation of small Nested Model sets
Research Papers in Economics, 2009Co-Authors: Kirstin Hubrich, Kenneth D WestAbstract:We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark Model to the MSPEs of a small set of alternative Models that nest the benchmark. Our procedures compare the bench-mark to all the alternative Models simultaneously rather than sequentially, and do not require re-estimation of Models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different Models for U.S. inflation. JEL Classification: C32, C53, E37
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forecast evaluation of small Nested Model sets
Social Science Research Network, 2008Co-Authors: Kirstin Hubrich, Kenneth D WestAbstract:We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark Model to the MSPEs of a small set of alternative Models that nest the benchmark. Our procedures compare the benchmark to all the alternative Models simultaneously rather than sequentially, and do not require reestimation of Models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White's (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different Models for U.S. inflation.
Lonni Schultz - One of the best experts on this subject based on the ideXlab platform.
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measurement of rat brain tumor kinetics using an intravascular mr contrast agent and dce mri Nested Model selection
Journal of Magnetic Resonance Imaging, 2014Co-Authors: Wilson B Chwang, Rajan Jain, Hassan Bagherebadian, Siamak P Nejaddavarani, A S M Iskander, Ashley Vanslooten, Lonni SchultzAbstract:Dynamic contrast-enhanced T1-weighted magnetic resonance imaging (DCE-MRI) is being increasingly used in various clinical trials involving brain tumors. It allows characterization of the vascular microenvironment in tumors by measurement of a range of parameters, such as Ktrans (forward transfer constant), kep (reverse transfer constant), ve (volume of the extravascular extracellular space), and vp (blood plasma volume) (1,2). These parameters reflect specific physiologic characteristics and hence relate to various aspects of tumor biology. One of the hurdles in obtaining these quantitative metrics is a lack of robust methods for approaching the problem of parametric estimation using multicompartmental pharmacokinetic Models (3). Another issue is that current Food and Drug Administration (FDA)-approved extravascular contrast agents (CAs) used for the assessment of vascular parameters in solid tumors are relatively small (molecular weight <1 kDa) (4). Thus, their transfer rates from the vasculature to the extravascular extracellular space (EES) are often very high in solid tumors. This generates a need for rapid sampling of the dynamic behavior of the CA in both tissue and arterial system, and also raises the possibility that the delivery of CA to tissue may be flow-limited in portions of very aggressive tumors. Intravascular or blood pool CAs are being studied for many clinical applications. For example, gadofosveset has been recently approved by the FDA for MR angiographic application in aorto-iliac disease (5). Gadofosveset is a blood pool agent that binds reversibly to serum albumin, distributes primarily in the intravascular space, exhibiting a prolonged plasma half-life and increased T1 relaxivity at 1.5 T. Since the albumin-gadofosveset compound has an effective molecular weight in excess of 6 kDa, the dynamics of the bound fraction will therefore be slow compared to an extravascular agent, making the delivery of larger CA permeability limited rather than flow-limited. Experience with intravascular or blood pool MR CAs in intracranial tumors is limited. One study by Adzamli et al (6) at 1.5 T demonstrated that small-molecule, albumin-binding blood pool CAs increased dose effectiveness and lengthened contrast enhancement in an intracranial mouse glioma Model. A study by Essig et al (7) showed that gadofosveset produced greater, longer-lasting contrast enhancement compared to conventional agents in a variety of human brain tumors. To our knowledge, gadofosveset has not been used to quantitatively estimate tumor kinetics using a DCE-MRI Nested Model selection (NMS) procedure in rat gliomas. The purpose of our study was to measure and compare the parameters of vascular physiology such as vp, Ktrans, and ve in a rat Model of human glioma using two different kinds of CAs: a primarily intravascular or blood pool CA (gadofosveset) and an extravascular CA (gadopentetate dimeglumine) using NMS and DCE-MRI.
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Measurement of rat brain tumor kinetics using an intravascular MR contrast agent and DCE-MRI Nested Model selection
Journal of Magnetic Resonance Imaging, 2014Co-Authors: Wilson B Chwang, Hassan Bagher-ebadian, Rajan Jain, A S M Iskander, Ashley Vanslooten, Lonni Schultz, Siamak P. Nejad-davarani, Ali S. Arbab, James R. EwingAbstract:Dynamic contrast-enhanced T1-weighted magnetic resonance imaging (DCE-MRI) is being increasingly used in various clinical trials involving brain tumors. It allows characterization of the vascular microenvironment in tumors by measurement of a range of parameters, such as Ktrans (forward transfer constant), kep (reverse transfer constant), ve (volume of the extravascular extracellular space), and vp (blood plasma volume) (1,2). These parameters reflect specific physiologic characteristics and hence relate to various aspects of tumor biology. One of the hurdles in obtaining these quantitative metrics is a lack of robust methods for approaching the problem of parametric estimation using multicompartmental pharmacokinetic Models (3). Another issue is that current Food and Drug Administration (FDA)-approved extravascular contrast agents (CAs) used for the assessment of vascular parameters in solid tumors are relatively small (molecular weight
