The conditional mean spectra by disaggregating the eta spectral shape indicator

Mohammad Ali Mohandesi; Alireza Azarbakht; Mohsen Ghafory‐Ashtiany

doi:10.1002/tal.1586

Electrically Driven Near-Infrared Broadband Light Source with Gaussian-Like Spectral Shape Based on Multiple InAs Quantum Dots

IEICE Transactions on Electronics ◽

10.1587/transele.e99.c.381 ◽

2016 ◽

Vol E99.C (3) ◽

pp. 381-384 ◽

Cited By ~ 5

Author(s):

Takuma YASUDA ◽

Nobuhiko OZAKI ◽

Hiroshi SHIBATA ◽

Shunsuke OHKOUCHI ◽

Naoki IKEDA ◽

...

Keyword(s):

Quantum Dots ◽

Light Source ◽

Near Infrared ◽

Spectral Shape ◽

Inas Quantum Dots ◽

Broadband Light Source

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Two-Dimensional Sampling-Recovery Algorithm of a Realization of Gaussian Processes on the Input and Output of Linear Systems

Entropy ◽

10.3390/e22101079 ◽

2020 ◽

Vol 22 (10) ◽

pp. 1079

Author(s):

Vladimir Kazakov ◽

Mauro A. Enciso ◽

Francisco Mendoza

Keyword(s):

Gaussian Processes ◽

Research Method ◽

General Algorithm ◽

Two Dimensional ◽

Recovery Algorithm ◽

Conditional Mean ◽

Quality Of Recovery ◽

Input And Output ◽

Statistical Relationships

Based on the application of the conditional mean rule, a sampling-recovery algorithm is studied for a Gaussian two-dimensional process. The components of such a process are the input and output processes of an arbitrary linear system, which are characterized by their statistical relationships. Realizations are sampled in both processes, and the number and location of samples in the general case are arbitrary for each component. As a result, general expressions are found that determine the optimal structure of the recovery devices, as well as evaluate the quality of recovery of each component of the two-dimensional process. The main feature of the obtained algorithm is that the realizations of both components or one of them is recovered based on two sets of samples related to the input and output processes. This means that the recovery involves not only its own samples of the restored realization, but also the samples of the realization of another component, statistically related to the first one. This type of general algorithm is characterized by a significantly improved recovery quality, as evidenced by the results of six non-trivial examples with different versions of the algorithms. The research method used and the proposed general algorithm for the reconstruction of multidimensional Gaussian processes have not been discussed in the literature.

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Spectral damping scaling factors for horizontal components of ground motions from subduction earthquakes using NGA-Subduction data

Earthquake Spectra ◽

10.1177/87552930211027903 ◽

2021 ◽

pp. 875529302110279

Author(s):

Sanaz Rezaeian ◽

Linda Al Atik ◽

Nicolas M Kuehn ◽

Norman Abrahamson ◽

Yousef Bozorgnia ◽

...

Keyword(s):

Ground Motions ◽

Damping Ratio ◽

Spectral Shape ◽

Parametric Models ◽

Scaling Factors ◽

Global Models ◽

Motion Models ◽

Subduction Earthquakes ◽

Crustal Earthquakes ◽

Subduction Zone Earthquakes

This article develops global models of damping scaling factors (DSFs) for subduction zone earthquakes that are functions of the damping ratio, spectral period, earthquake magnitude, and distance. The Next Generation Attenuation for subduction earthquakes (NGA-Sub) project has developed the largest uniformly processed database of recorded ground motions to date from seven subduction regions: Alaska, Cascadia, Central America and Mexico, South America, Japan, Taiwan, and New Zealand. NGA-Sub used this database to develop new ground motion models (GMMs) at a reference 5% damping ratio. We worked with the NGA-Sub project team to develop an extended database that includes pseudo-spectral accelerations (PSA) for 11 damping ratios between 0.5% and 30%. We use this database to develop parametric models of DSF for both interface and intraslab subduction earthquakes that can be used to adjust any subduction GMM from a reference 5% damping ratio to other damping ratios. The DSF is strongly influenced by the response spectral shape and the duration of motion; therefore, in addition to the damping ratio, the median DSF model uses spectral period, magnitude, and distance as surrogate predictor variables to capture the effects of the spectral shape and the duration of motion. We also develop parametric models for the standard deviation of DSF. The models presented in this article are for the RotD50 horizontal component of PSA and are compared with the models for shallow crustal earthquakes in active tectonic regions. Some noticeable differences arise from the considerably longer duration of interface records for very large magnitude events and the enriched high-frequency content of intraslab records, compared with shallow crustal earthquakes. Regional differences are discussed by comparing the proposed global models with the data from each subduction region along with recommendations on the applicability of the models.

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Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

Mathematics ◽

10.3390/math9030282 ◽

2021 ◽

Vol 9 (3) ◽

pp. 282

Author(s):

Mabel Morales-Otero ◽

Vicente Núñez-Antón

Keyword(s):

Infant Mortality ◽

Count Data ◽

Spatial Dependence ◽

Poisson Model ◽

Mortality Rates ◽

Estimation Methods ◽

Neighborhood Structure ◽

Specific Context ◽

Count Data Models ◽

Conditional Mean

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.

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