Abstract Least-squares reverse-time migration (RTM) works with an inverse operation, rather than adjoint operation in a conventional RTM, and thus produces image higher resolution more balanced amplitude the RTM image. However, least-squares introduces two side effects: sidelobes around reflectors high-wavenumber artifacts. These effects are caused mainly by limited bandwidth of seismic data, c...

Exponentially Weighted Recursive Least Squares (EWRLS) is adopted to estimate adaptive prediction filters for f-x seismic interpolation. Adaptive prediction filters are able to model signals where the dominant wavenumbers are varying in space. This concept leads to a f-x interpolation method that does not require windowing strategies for optimal results. In other words, adaptive prediction filt...

In this paper, we compare the lifetimes of two series and two parallel systems stochastically where the lifetime of each component follows location-scale (LS) family of distributions. The comparison is carried out under two scenarios: one, that the components of the systems have a dependent structure sharing Archimedean copula and two, that the components are independently distributed. It is sh...

Traditionally, the method of nonlinear least squares (NLLS) analysis has been used to estimate the parameters obtained from exponential decay data. In this study, we evaluated the use of Bayesian probability theory to analyze such data; specifically, that resulting from intravoxel incoherent motion NMR experiments. Analysis was done both on simulated data to which different amounts of Gaussian ...

We describe the underlying probabilistic interpretation of alpha and beta divergences. We first show that beta divergences are inherently tied to Tweedie distributions, a particular type of exponential family, known as exponential dispersion models. Starting from the variance function of a Tweedie model, we outline how to get alpha and beta divergences as special cases of Csiszár’s f and Bregma...

A method based on the Fourier convolution theorem is developed for the analysis of data composed of random noise, plus an unknown constant "base line," plus a sum of (or an integral over a continuous spectrum of) exponential decay functions. The Fourier method's usual serious practical limitation of needing high accuracy data over a very wide range is eliminated by the introduction of convergen...

The paper presents a system dynamics approach to analysis of generic manufacturing supply chain model and considers the problem of determination of adaptive effective strategy on production, supply rates and price according to inventory levels and the market situation through a demand curve estimation. The special focus lies on the investigation of the product price influences on demand and its...

In this paper we have proposed a class of Generalized Autoregressive Moving Average (GARMA) models which extend univariate ARMA models to a non-Gaussian situation (i.e. they extend the univariate Generalized Linear Model to incorporate time dependence in the observations). The simplicity of the tting algorithm within the iteratively reweighted least squares (IRLS) framework will be shown. Model...

[1] The eddy covariance measurements of carbon dioxide fluxes collected around the world offer a rich source for detailed data analysis. Simple, aggregated models are attractive tools for gap filling, budget calculation, and upscaling in space and time. Key in the application of these models is their parameterization and a robust estimate of the uncertainty and reliability of their predictions....

A nonlinear regression model is proposed as an alternative to the Box-Cox regression model for nonnegative variables. The functional form contains as special cases the linear, exponential, constant elasticity, and generalized CES specifications, as well as other functional forms used by applied econometricians . The model can be derived from but is more general than a particular modification of...