Korrigierte Schätzgleichungen für allgemeine Regressionsmodelle mit Fehlern in den Variablen / Corrected Estimating Equations for General Regression Models with Errors in the Variables

1998 ◽  
Vol 217 (1) ◽  
Author(s):  
Hans Schneeweiß

ZusammenfassungNach einer kurzen Einführung in die Theorie der erwartungstreuen Schätzgleichungen für allgemeine Regressionsmodelle und der korrigierten Schätzgleichungen für Regressionsmodelle mit fehlerbehafteten Kovariablen wird die Approximationsgüte eines auf Reihenentwicklung basierenden Ansatzes von Stefanski diskutiert.

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Emmanuel Arinaitwe ◽  
Joaniter I. Nankabirwa ◽  
Paul Krezanoski ◽  
John Rek ◽  
Victor Kamya ◽  
...  

Abstract Background The burden of malaria in Uganda remains high, but has become increasingly heterogenous following intensified malaria control. Travel within Uganda is recognized as a risk factor for malaria, but behaviours associated with travel are not well-understood. To address this knowledge gap, malaria-relevant behaviours of cohort participants were assessed during travel and at home in Uganda. Methods Residents from 80 randomly selected households in Nagongera sub-county, Tororo district were enrolled into a cohort to study malaria in rural Uganda. All participants were given long-lasting insecticidal nets (LLINs) at enrolment and were evaluated every 4 weeks at the study clinic. Participants were asked if they had travelled overnight from their home, and if so, a questionnaire was administered to capture information on travel details and behaviours. Behaviour while travelling was assessed within 4 weeks following travel during the study clinic visit. Behaviour while at home was assessed using a similar questionnaire during two-weekly home visits. Behaviours while travelling vs at home were compared using log binomial regression models with generalized estimating equations adjusting for repeated measures in the same individual. Analysis of factors associated with LLIN adherence, such as destination and duration of travel, time to bed during travel, gender and age at time of travel, were assessed using log binomial regression models with generalized estimating equations adjusting for repeated measures in the same individual. Results Between October 2017 and October 2019, 527 participants were enrolled and assessed for travel. Of these, 123 (23.2%) reported taking 211 overnight trips; 149 (70.6%) trips were within Tororo. Participants were less likely to use LLINs when travelling than when at home (41.0% vs. 56.2%, relative risk [RR] 0.73, 95% CI 0.60–0.89, p = 0.002); this difference was noted for women (38.8% vs 59.2%, RR 0.66, 95% CI 0.52–0.83, p = 0.001) but not men (48.3% vs 46.6%, RR 0.96, 95% CI 0.67–1.40, p = 0.85). In an adjusted analysis, factors associated with LLIN use when travelling included destination (travelling to districts not receiving indoor residual spraying [IRS] 65.8% vs Tororo district 32.2%, RR 1.80, 95% CI 1.31–2.46, p < 0.001) and duration of travel (> 7 nights 60.3% vs one night 24.4%, RR 1.97, 95% CI 1.07–3.64, p = 0.03). Conclusions Travellers, particularly women, were less likely to use LLINs when travelling than when at home. LLIN adherence was higher among those who travelled to non-IRS districts and for more than 1 week, suggesting that perceived malaria risk influences LLIN use. Strategies are needed to raise awareness of the importance of using LLINs while travelling.


2010 ◽  
Vol 121-122 ◽  
pp. 346-349
Author(s):  
Yu Qin Sun ◽  
Yuan Ttao Jiang ◽  
Yong Ge Tian

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.


2021 ◽  
Vol 23 (1) ◽  
pp. 135-148
Author(s):  
Yunjeong Kang ◽  
Myung Hwan Na ◽  
Wanhyun Cho ◽  
Hyeon Seok Ko

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