Testing significance of random effects for the gamma degradation model

This paper considers the Wiener degradation model with random effects. Random-effect models take into account the unit-to-unit variability of the degradation index. It is assumed that a random parameter has a truncated normal distribution. During the research, the expression for the maximum likelihood estimates and the reliability function has been obtained. Two statistical tests have been proposed to reveal the existence of random effects in degradation data corresponding to the Wiener degradation model. The first test is a well-known likelihood ratio test, and the second one is based on the variance estimate of the random parameter. These tests have been compared in terms of power with the Monte-Carlo simulation method. The result of the research has shown that the criterion based on the variance estimate of the random parameter is more powerful than the likelihood ratio test in the case of the considered pairs of competing hypotheses. An example of the analysis using the proposed tests for the turbofan engine degradation data has been considered. The data set includes the measurements recorded from 18 sensors for 100 engines. Before constructing the degradation model, the single degradation index has been obtained using the principal component method. The hypothesis of the random effect insignificance in the model has been rejected for both tests. It has been shown that the random-effect Wiener degradation model describes the failure time distribution more accurately than the fixed-effect Wiener degradation model.

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The Wiener degradation model with random effects in reliability metrology

Series on Advances in Mathematics for Applied Sciences - Advanced Mathematical and Computational Tools in Metrology and Testing XI ◽

10.1142/9789813274303_0013 ◽

2018 ◽

pp. 162-169

Author(s):

E. S. Chetvertakova ◽

E. V. Chimitova

Keyword(s):

Random Effects ◽

Degradation Model

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Proportion of Individuals With Past-Year Suicidal Ideation Who Attempted Suicide Over the Past 10 Years in the United States, and the Influence of Age and Sex

Crisis ◽

10.1027/0227-5910/a000690 ◽

2020 ◽

pp. 1-5

Author(s):

Shannon Lange ◽

Courtney Bagge ◽

Charlotte Probst ◽

Jürgen Rehm

Keyword(s):

United States ◽

Suicidal Ideation ◽

Random Effects ◽

Attempted Suicide ◽

Suicide Attempts ◽

The United States ◽

Individual Level ◽

The Past ◽

Meta Analyses ◽

Age Category

Abstract. Background: In recent years, the rate of death by suicide has been increasing disproportionately among females and young adults in the United States. Presumably this trend has been mirrored by the proportion of individuals with suicidal ideation who attempted suicide. Aim: We aimed to investigate whether the proportion of individuals in the United States with suicidal ideation who attempted suicide differed by age and/or sex, and whether this proportion has increased over time. Method: Individual-level data from the National Survey on Drug Use and Health (NSDUH), 2008–2017, were used to estimate the year-, age category-, and sex-specific proportion of individuals with past-year suicidal ideation who attempted suicide. We then determined whether this proportion differed by age category, sex, and across years using random-effects meta-regression. Overall, age category- and sex-specific proportions across survey years were estimated using random-effects meta-analyses. Results: Although the proportion was found to be significantly higher among females and those aged 18–25 years, it had not significantly increased over the past 10 years. Limitations: Data were self-reported and restricted to past-year suicidal ideation and suicide attempts. Conclusion: The increase in the death by suicide rate in the United States over the past 10 years was not mirrored by the proportion of individuals with past-year suicidal ideation who attempted suicide during this period.

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Positional Estimation Within a Latent Space Model for Networks

Methodology ◽

10.1027/1614-2241.2.1.24 ◽

2006 ◽

Vol 2 (1) ◽

pp. 24-33 ◽

Cited By ~ 17

Author(s):

Susan Shortreed ◽

Mark S. Handcock ◽

Peter Hoff

Keyword(s):

Random Effects ◽

Network Data ◽

Random Effects Models ◽

Space Model ◽

Network Analyses ◽

Recent Advances ◽

Latent Space ◽

Latent Space Model ◽

Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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