Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

295
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
295
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

250
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
250
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.2K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

19
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
19
Poisson Probability Distribution01:09

Poisson Probability Distribution

7.7K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.7K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

48
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
48

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours.

PloS one·2026
Same author

Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification.

Scientific reports·2026
Same author

Nodal Heterogeneity can Induce Ghost Triadic Effects in Relational Event Models.

Psychometrika·2026
Same author

A general framework for adaptive nonparametric dimensionality reduction.

Scientific reports·2026
Same author

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks.

Advances in data analysis and classification·2025
Same author

Rethinking psychometrics through LLMs: how item semantics shape measurement and prediction in psychological questionnaires.

Scientific reports·2025
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: May 17, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

Bayesian poisson regression tensor train decomposition model for learning mortality pattern changes during COVID-19

Wei Zhang1, Antonietta Mira2,3, Ernst C Wit1

  • 1Faculty of Informatics, Università della Svizzera italiana, Lugano, Switzerland.

Journal of Applied Statistics
|March 31, 2025
PubMed
Summary
This summary is machine-generated.

This study analyzed Italian mortality data from 2015-2020 to understand COVID-19's impact on other causes of death. The findings reveal complex relationships between the pandemic, interventions, and mortality patterns.

Keywords:
Bayesian inferenceCOVID-19mortalitytensor decomposition

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.3K

Related Experiment Videos

Last Updated: May 17, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.3K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • The COVID-19 pandemic has caused significant global excess mortality.
  • The specific impact of COVID-19 on mortality rates from non-COVID causes requires detailed investigation.

Purpose of the Study:

  • To analyze the broader effects of COVID-19 on various causes of death using Italian mortality data.
  • To develop and apply a novel statistical model for dissecting complex mortality patterns.

Main Methods:

  • Utilized official Italian monthly mortality data (January 2015 - December 2020).
  • Developed a Bayesian model combining Poisson regression and tensor train decomposition.
  • Employed Metropolis-Hastings within Gibbs algorithm for posterior inference.

Main Results:

  • Identified differential effects of interventions on cause-specific mortality rates.
  • Revealed insights into the relationship between COVID-19 and other causes of death.
  • Uncovered latent classes associated with demographics, temporal trends, and mortality causes.

Conclusions:

  • The developed statistical approach effectively models complex, high-dimensional mortality data.
  • COVID-19 has multifaceted impacts on mortality beyond direct fatalities, influencing other causes of death.
  • The findings provide a deeper understanding of pandemic-related mortality dynamics.