Seismic multiple prediction through inversion for real data application

2006 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Real Data ◽  
Data Application ◽  
Multiple Prediction

scholarly journals Multiple prediction through inversion: Theoretical advancements and real data application

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. V33-V39 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Explicit Knowledge ◽  
Real Data ◽  
Second Phase ◽  
Multiple Model ◽  
Subsurface Structures ◽  
Multiple Attenuation ◽  
Data Application ◽  
Multiple Prediction ◽  
Marine Seismic ◽  
Surface Operator

Wave-equation-based multiple attenuation seismic methods may be divided into the two distinct phases of multiple modeling and multiple subtraction. These two are interrelated and must be optimized in order to produce an optimal final result. The multiple prediction through inversion (MPI) scheme updates the multiple model iteratively, as we usually do in a linearized inverse problem. The scheme models the multiple wavefield without an explicit knowledge of surface and subsurface structures or of the source signature; both are generally unknown in seismic surveys. However, compared to a conventional surface-related multiple attenuation method, the accuracy of the multiple model is improved both kinematically and dynamically. It is because the MPI scheme implicitly takes account of the spatial variation of the surface reflectivity, the source signature, the detector patterns and receiver ghosts, and other effects included in the so-called surface operator. When the MPI scheme is used in the first phase it also significantly reduces the nonlinearity of the problem in the second phase that involves attenuating multiples without removing or altering primaries. The effectiveness of the MPI scheme is demonstrated by examples involving real marine seismic data.

scholarly journals Slash Truncation Positive Normal Distribution and Its Estimation Based on the EM Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2164
Author(s):  
Héctor J. Gómez ◽  
Diego I. Gallardo ◽  
Karol I. Santoro
Keyword(s):  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
R Software ◽  
Data Application ◽  
The Em Algorithm ◽  
Kurtosis Parameter ◽  
Computational Implementation ◽  
Moments Method

In this paper, we present an extension of the truncated positive normal (TPN) distribution to model positive data with a high kurtosis. The new model is defined as the quotient between two random variables: the TPN distribution (numerator) and the power of a standard uniform distribution (denominator). The resulting model has greater kurtosis than the TPN distribution. We studied some properties of the distribution, such as moments, asymmetry, and kurtosis. Parameter estimation is based on the moments method, and maximum likelihood estimation uses the expectation-maximization algorithm. We performed some simulation studies to assess the recovery parameters and illustrate the model with a real data application related to body weight. The computational implementation of this work was included in the tpn package of the R software.

scholarly journals Causal variance decompositions for institutional comparisons in healthcare

2019 ◽  
Vol 29 (7) ◽  
pp. 1972-1986
Author(s):  
Bo Chen ◽  
Keith A Lawson ◽  
Antonio Finelli ◽  
Olli Saarela
Keyword(s):  
Random Effect ◽  
Real Data ◽  
Hospital Performance ◽  
Case Mix ◽  
Link Functions ◽  
Variation In Care ◽  
Residual Variation ◽  
Data Application ◽  
Variance Decompositions ◽  
Random Effect Models

There is increasing interest in comparing institutions delivering healthcare in terms of disease-specific quality indicators (QIs) that capture processes or outcomes showing variations in the care provided. Such comparisons can be framed in terms of causal models, where adjusting for patient case-mix is analogous to controlling for confounding, and exposure is being treated in a given hospital, for instance. Our goal here is to help identify good QIs rather than comparing hospitals in terms of an already chosen QI, and so we focus on the presence and magnitude of overall variation in care between the hospitals rather than the pairwise differences between any two hospitals. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance adjusting for the case-mix, the case-mix itself, and residual variation. For this purpose, we derive a three-way variance decomposition, with particular attention to its causal interpretation in terms of potential outcome variables. We propose model-based estimators for the decomposition, accommodating different link functions and either fixed or random effect models. We evaluate their performance in a simulation study and demonstrate their use in a real data application.

Model Diagnostics for Bayesian Networks

2006 ◽  
Vol 31 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Sandip Sinharay
Keyword(s):  
Model Checking ◽  
Bayesian Networks ◽  
Real Data ◽  
Diagnostic Tools ◽  
Model Diagnostics ◽  
Posterior Predictive Model Checking ◽  
Item Fit ◽  
Item Functioning ◽  
Data Application ◽  
Educational Assessments

Bayesian networks are frequently used in educational assessments primarily for learning about students’ knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A number of aspects of model fit, those of usual interest to practitioners, are assessed using various diagnostic tools. This article suggests a direct data display for assessing overall fit, suggests several diagnostics for assessing item fit, suggests a graphical approach to examine if the model can explain the association among the items, and suggests a version of the Mantel–Haenszel statistic for assessing differential item functioning. Limited simulation studies and a real data application demonstrate the effectiveness of the suggested model diagnostics.

Diffraction images and their attributes based on asymmetric summation: real data application

2020 ◽  
Author(s):  
Maxim I. Protasov ◽  
◽  
Vladimir A. Tcheverda ◽  
Valery V. Shilikov ◽  
◽  
...  
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Data Sets ◽  
Data Application ◽  
Diffraction Imaging

The paper deals with a 3D diffraction imaging with the subsequent diffraction attribute calculation. The imaging is based on an asymmetric summation of seismic data and provides three diffraction attributes: structural diffraction attribute, point diffraction attribute, an azimuth of structural diffraction. These attributes provide differentiating fractured and cavernous objects and to determine the fractures orientations. Approbation of the approach was provided on several real data sets.

scholarly journals Mixture of Lindley and Inverse Weibull Distributions: Properties and Estimation

2021 ◽  
Vol 20 ◽  
pp. 134-143
Author(s):  
A. S. Al-Moisheer ◽  
A. F. Daghestani ◽  
K. S. Sultan
Keyword(s):  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Real Data ◽  
Estimation Methods ◽  
Simulation Studies ◽  
Weibull Distributions ◽  
Unknown Parameters ◽  
Data Application ◽  
Rate Functions ◽  
And Behavior

In this paper, we talk about a mixture of one-parameter Lindley and inverse Weibull distributions (MLIWD). First, We introduce and discuss the MLIWD. Then, we study the main statistical properties of the proposed mixture and provide some graphs of both the density and the associated hazard rate functions. After that, we estimate the unknown parameters of the proposed mixture via two estimation methods, namely, the generalized method of moments and maximum likelihood. In addition, we compare the estimation methods via some simulation studies to determine the efficacy of the two estimation methods. Finally, we evaluate the performance and behavior of the proposed mixture with different numerical examples and real data application in survival analysis.

scholarly journals A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models

2021 ◽  
Author(s):  
Niklas Maltzahn ◽  
Rune Hoff ◽  
Odd O. Aalen ◽  
Ingrid S. Mehlum ◽  
Hein Putter ◽  
...  
Keyword(s):  
Statistical Power ◽  
Transition Probabilities ◽  
Real Data ◽  
Testing Procedure ◽  
Data Application ◽  
Stratification Method ◽  
Model Complex ◽  
Markov Assumption ◽  
State Models ◽  
Disability Education

AbstractMulti-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen estimator. Since landmarking is a stratification method a disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for “less traveled” transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Introducing the concept of partially non-Markov multi-state models, we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. We also show how non-Markov transitions can be identified using a testing procedure. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. We show that the proposed estimator is consistent, but that the traditional variance estimator can underestimate the variance of both the hybrid and landmark estimator. Bootstrapping is therefore recommended. The methods are compared in a simulation study and in a real data application using registry data to model individual transitions for a birth cohort of 184 951 Norwegian men between states of sick leave, disability, education, work and unemployment.

scholarly journals The partly parametric and partly nonparametric additive risk model

2021 ◽  
Author(s):  
Nils Lid Hjort ◽  
Emil Aas Stoltenberg
Keyword(s):  
Hazard Rate ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Real Data ◽  
Rate Function ◽  
Nonparametric Model ◽  
Additive Risk Model ◽  
Data Application ◽  
Additive Risk ◽  
Hazard Factor

AbstractAalen’s linear hazard rate regression model is a useful and increasingly popular alternative to Cox’ multiplicative hazard rate model. It postulates that an individual has hazard rate function $$h(s)=z_1\alpha _1(s)+\cdots +z_r\alpha _r(s)$$ h ( s ) = z 1 α 1 ( s ) + ⋯ + z r α r ( s ) in terms of his covariate values $$z_1,\ldots ,z_r$$ z 1 , … , z r . These are typically levels of various hazard factors, and may also be time-dependent. The hazard factor functions $$\alpha _j(s)$$ α j ( s ) are the parameters of the model and are estimated from data. This is traditionally accomplished in a fully nonparametric way. This paper develops methodology for estimating the hazard factor functions when some of them are modelled parametrically while the others are left unspecified. Large-sample results are reached inside this partly parametric, partly nonparametric framework, which also enables us to assess the goodness of fit of the model’s parametric components. In addition, these results are used to pinpoint how much precision is gained, using the parametric-nonparametric model, over the standard nonparametric method. A real-data application is included, along with a brief simulation study.

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