A new integrated likelihood for estimating population size in dependent dual‐record system

Chatterjee, Kiranmoy; Mukherjee, Diganta

doi:10.1002/cjs.11477

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…for N based on the log-transformation method, proposed by Chao 35 . Following Chatterjee and Mukherjee 36 , we compute the length of the confidence interval (LCI) as well as its coverage probability (CP). To evaluate and compare the performance of the proposed estimator with the existing competitors (see Section E of the Supplementary Material for details), we consider log-linear model (LLM) 26 , model 22 , 37 , quasi-symmetry model (QSM) 16 , partial quasi-symmetry model (PQSM) 16 , non-parametric sample coverage method (SC) 31 and the independent model (IM) i.e.…”

Section: Resultsmentioning

confidence: 99%

Estimating prevalence of post-war health disorders using multiple systems data

Bhuyan,

Chatterjee

2024

Sci Rep

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Effective surveillance on the long-term public health impact due to war and terrorist attacks remains limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size or undercount of the population under the risk of physical and mental health hazards is of utmost necessity. A novel trivariate Bernoulli model is developed allowing heterogeneity among the individuals and dependence between the sources of information, and an estimation methodology using a Monte Carlo-based EM algorithm is proposed. Simulation results show the superiority of the performance of the proposed method over existing competitors and robustness under model mis-specifications. The method is applied to analyse two real case studies on monitoring amyotrophic lateral sclerosis (ALS) cases for the Gulf War veterans and the 9/11 terrorist attack survivors at the World Trade Center, USA. The average annual cumulative incidence rate for ALS disease increases by $$33\%$$ 33 % and $$16\%$$ 16 % for deployed and no-deployed military personnel, respectively, after adjusting the undercount. The number of individuals exposed to the risk of physical and mental health effects due to WTC terrorist attacks increased by $$42\%$$ 42 % . These results provide interesting insights that can assist in effective decision-making and policy formulation for monitoring the health status of post-war survivors.

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Section: Resultsmentioning

confidence: 99%

Estimating prevalence of post-war health disorders using multiple systems data

Bhuyan,

Chatterjee

2024

Sci Rep

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“…Suppose, we have 2 n number of news (posts) that have been posted on social media, 2 p of them with class tags, and the remaining 2( n − p ) without, where p < n . There is an equal number of false and actual news [ 42 ]. First, we create two third-order tensors using binary representation to model user's friendships: X (fake) ∈ Ru × u × p containing all news posts marked as false and X (real) ∈ Ru × u × p with news that has been flagged as valid.…”

Section: Methodsmentioning

confidence: 99%

English Text Recognition Deep Learning Framework to Automatically Identify Fake News

Xiao-yu

2022

Computational Intelligence and Neuroscience

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Fake news spreading rapidly worldwide is considered one of the most severe problems of modern technology that needs to be addressed immediately. The remarkable increase in the use of social media as a critical source of information combined with the shaking of trust in traditional media, the high speed of digital news dissemination, and the vast amount of information circulating on the Internet have exacerbated the problem of so-called fake news. The present work proves the importance of detecting fake news by taking advantage of the information derived from friendships between users. Specifically, using an innovative deep temporal convolutional network (DTCN) scheme assisted using the tensor factorization non-negative RESCAL method, we take advantage of class-aware rate tables during and not after the factorization process, producing more accurate representations to detect fake news with exceptionally high reliability. In this way, the need to develop automated methods for detecting false information is demonstrated with the primary aim of protecting readers from misinformation.

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“…the behavior of an individual, while he/she attempts to be included in List 2, is grossly known as behavioral response variation (Wolter 1986;Chatterjee and Mukherjee 2018). O'Connell and Pollock (1992) introduced a strategy using demographic co-variates for partitioning a population in terms of direction of behavioral dependence.…”

Section: List 2 Listmentioning

confidence: 99%

Identifying the Direction of Behavioral Dependence in Two-Sample Capture-Recapture Study

Chatterjee¹,

Mukherjee

2020

Journal of Official Statistics

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With the possibility of dependence between the sources in a capture-recapture type experiment, identification of the direction of such dependence in dual system of data collection is vital. This has a wide range of applications, including in the domains of public health, official statistics and social sciences. Owing to the insufficiency of data for analyzing a behavioral dependence model in dual system, our contribution lies in the construction of several strategies that can identify the direction of underlying dependence between the two lists in the dual system, that is, whether the two lists are positively or negatively dependent. Our proposed classification strategies would be quite appealing for improving the inference as evident from recent literature. Simulation studies are carried out to explore the comparative performance of the proposed strategies. Finally, applications on three real data sets from various fields are illustrated.

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A new integrated likelihood for estimating population size in dependent dual‐record system

Cited by 7 publications

References 32 publications

Estimating prevalence of post-war health disorders using multiple systems data

Estimating prevalence of post-war health disorders using multiple systems data

English Text Recognition Deep Learning Framework to Automatically Identify Fake News

Identifying the Direction of Behavioral Dependence in Two-Sample Capture-Recapture Study

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