Search results for "METHODOLOGY"
showing 10 items of 852 documents
KFAS : Exponential Family State Space Models in R
2017
State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes an R package KFAS for state space modelling with the observations from an exponential family, namely Gaussian, Poisson, binomial, negative binomial and gamma distributions. After introducing the basic theory behind Gaussian and non-Gaussian state space models, an illustrative example of Poisson time series forecasting is provided. Finally, a comparison to alternative R packages suitable for non-Gaussian time series modelling is presented.
Statistical Performance Analysis of a Fast Super-Resolution Technique Using Noisy Translations.
2014
It is well known that the registration process is a key step for super-resolution reconstruction. In this work, we propose to use a piezoelectric system that is easily adaptable on all microscopes and telescopes for controlling accurately their motion (down to nanometers) and therefore acquiring multiple images of the same scene at different controlled positions. Then a fast super-resolution algorithm \cite{eh01} can be used for efficient super-resolution reconstruction. In this case, the optimal use of $r^2$ images for a resolution enhancement factor $r$ is generally not enough to obtain satisfying results due to the random inaccuracy of the positioning system. Thus we propose to take seve…
Study design in causal models
2012
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation. Conclusions whether a causal or observational relationship can be estimated from the collect…
Surrogate outcomes and transportability
2019
Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification of the causal effect of interest. Instead of the outcome of interest, surrogate outcomes are measured in the experiments. This problem is a generalization of identifiability using surrogate experiments and we label it as surrogate outcome identifiability. We show that the concept of transportability provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.
Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular co…
2022
Abstract Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and s…
General framework for testing Poisson-Voronoi assumption for real microstructures
2020
Modeling microstructures is an interesting problem not just in Materials Science but also in Mathematics and Statistics. The most basic model for steel microstructure is the Poisson-Voronoi diagram. It has mathematically attractive properties and it has been used in the approximation of single phase steel microstructures. The aim of this paper is to develop methods that can be used to test whether a real steel microstructure can be approximated by such a model. Therefore, a general framework for testing the Poisson-Voronoi assumption based on images of 2D sections of real metals is set out. Following two different approaches, according to the use or not of periodic boundary conditions, thre…
Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm
2016
Diffusion tensor imaging (DTI) is widely used to characterize, in vivo, the white matter of the central nerve system (CNS). This biological tissue contains much anatomic, structural and orientational information of fibers in human brain. Spectral data from the displacement distribution of water molecules located in the brain tissue are collected by a magnetic resonance scanner and acquired in the Fourier domain. After the Fourier inversion, the noise distribution is Gaussian in both real and imaginary parts and, as a consequence, the recorded magnitude data are corrupted by Rician noise. Statistical estimation of diffusion leads a non-linear regression problem. In this paper, we present a f…
Supplemental Material, Online_supplement_4_-_Tutorial_on_calculations - An Updated Guideline for Assessing Discriminant Validity
2020
Supplemental Material, Online_supplement_4_-_Tutorial_on_calculations for An Updated Guideline for Assessing Discriminant Validity by Mikko Rönkkö and Eunseong Cho in Organizational Research Methods
Supplemental Material, Coloring_and_code_issues_appen - On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recomme…
2019
Supplemental Material, Coloring_and_code_issues_appen for On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations by John Antonakis, Nicolas Bastardoz and Mikko Rönkkö in Organizational Research Methods
Supplemental Material, sj-docx-1-orm-10.1177_1094428119877457 - On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and…
2021
Supplemental Material, sj-docx-1-orm-10.1177_1094428119877457 for On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations by John Antonakis, Nicolas Bastardoz and Mikko Rönkkö in Organizational Research Methods