scholarly journals Absolute regularity and ergodicity of Poisson count processes

Bernoulli ◽  
2011 ◽  
Vol 17 (4) ◽  
pp. 1268-1284 ◽  
Author(s):  
Michael H. Neumann
Keyword(s):  
Absolute Regularity ◽  
Poisson Count

scholarly journals Goodness‐of‐Fit Tests for Poisson Count Time Series based on the Stein‐‐Chen Identity

2021 ◽  
Author(s):  
Boris Aleksandrov ◽  
Christian H. Weiß ◽  
Carsten Jentsch
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Count Time Series ◽  
Goodness Of Fit Tests ◽  
Poisson Count

scholarly journals KR20 and KR21 for Some Nondichotomous Data (It’s Not Just Cronbach’s Alpha)

2021 ◽  
pp. 001316442199253
Author(s):  
Robert C. Foster
Keyword(s):  
Exponential Family ◽  
Exponential Families ◽  
Natural Exponential Family ◽  
Cronbach’S Alpha ◽  
Natural Exponential Families ◽  
Cronbach's Alpha ◽  
Generalized Framework ◽  
The Common ◽  
Poisson Data ◽  
Poisson Count

This article presents some equivalent forms of the common Kuder–Richardson Formula 21 and 20 estimators for nondichotomous data belonging to certain other exponential families, such as Poisson count data, exponential data, or geometric counts of trials until failure. Using the generalized framework of Foster (2020), an equation for the reliability for a subset of the natural exponential family have quadratic variance function is derived for known population parameters, and both formulas are shown to be different plug-in estimators of this quantity. The equivalent Kuder–Richardson Formulas 20 and 21 are given for six different natural exponential families, and these match earlier derivations in the case of binomial and Poisson data. Simulations show performance exceeding that of Cronbach’s alpha in terms of root mean square error when the formula matching the correct exponential family is used, and a discussion of Jensen’s inequality suggests explanations for peculiarities of the bias and standard error of the simulations across the different exponential families.

scholarly journals Optimal one‐ and two‐sided adaptive EWMA scheme for monitoring Poisson count data

2021 ◽  
Author(s):  
Anan Tang ◽  
Philippe Castagliola ◽  
Xuelong Hu ◽  
Xiaojian Zhou
Keyword(s):  
Count Data ◽  
Poisson Count

Control Charts for Poisson Count Data with Varying Sample Sizes

2010 ◽  
Vol 42 (3) ◽  
pp. 260-275 ◽  
Author(s):  
Anne G. Ryan ◽  
William H. Woodall
Keyword(s):  
Count Data ◽  
Control Charts ◽  
Sample Sizes ◽  
Poisson Count

scholarly journals SpatialDE2: Fast and localized variance component analysis of spatial transcriptomics

2021 ◽  
Author(s):  
Ilia Kats ◽  
Roser Vento-Tormo ◽  
Oliver Stegle
Keyword(s):  
Expression Profiles ◽  
Simulated Data ◽  
Variance Component Analysis ◽  
Gene Detection ◽  
Computational Speed ◽  
Real World Applications ◽  
Analysis Workflow ◽  
Variable Gene ◽  
Integrated Software ◽  
Poisson Count

Spatial transcriptomics is now a mature technology, allowing to assay gene expression changes in the histological context of complex tissues. A canonical analysis workflow starts with the identification of tissue zones that share similar expression profiles, followed by the detection of highly variable or spatially variable genes. Rapid increases in the scale and complexity of spatial transcriptomic datasets demand that these analysis steps are conducted in a consistent and integrated manner, a requirement that is not met by current methods. To address this, we here present SpatialDE2, which unifies the mapping of tissue zones and spatial variable gene detection as integrated software framework, while at the same time advancing current algorithms for both of these steps. Formulated in a Bayesian framework, the model accounts for the Poisson count noise, while simultaneously offering superior computational speed compared to previous methods. We validate SpatialDE2 using simulated data and illustrate its utility in the context of two real-world applications to the spatial transcriptomics profiles of the mouse brain and human endometrium.

Comparison of control charts for Poisson count data in health‐care monitoring

2020 ◽  
Author(s):  
Michele Scagliarini ◽  
Nunzia Boccaforno ◽  
Marco Vandi
Keyword(s):  
Health Care ◽  
Count Data ◽  
Control Charts ◽  
Health Care Monitoring ◽  
Poisson Count

Right-Censored Mixed Poisson Count Models with Detection Times

2019 ◽  
Vol 25 (1) ◽  
pp. 112-132
Author(s):  
Wen-Han Hwang ◽  
Rachel V. Blakey ◽  
Jakub Stoklosa
Keyword(s):  
Count Models ◽  
Poisson Count

ON THE ABSOLUTE REGULARITY OF LINEAR RANDOM DYNAMICAL SYSTEMS

2003 ◽  
Vol 03 (04) ◽  
pp. 453-461 ◽  
Author(s):  
LUU HOANG DUC
Keyword(s):  
Dynamical Systems ◽  
Ergodic Theorem ◽  
Random Dynamical Systems ◽  
Absolute Regularity ◽  
Integrability Conditions ◽  
Multiplicative Ergodic Theorem ◽  
Lyapunov Regularity ◽  
The Absolute

We introduce a concept of absolute regularity of linear random dynamical systems (RDS) that is stronger than Lyapunov regularity. We prove that a linear RDS that satisfies the integrability conditions of the multiplicative ergodic theorem of Oseledets is not merely Lyapunov regular but absolutely regular.

Multiscale change point analysis in Poisson count data

2007 ◽  
Vol 85 (2) ◽  
pp. 159-169 ◽  
Author(s):  
Maarten Jansen
Keyword(s):  
Count Data ◽  
Change Point ◽  
Change Point Analysis ◽  
Point Analysis ◽  
Poisson Count
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