Results 1 to 10 of about 112,278 (117)
Inverse-Probability-Weighted Wavelet Estimation of Regression Derivatives Under Missing-at-Random Responses for Stationary Ergodic Processes [PDF]
EntropyWe consider the estimation of partial derivatives of multivariate regression-type functionals from incomplete observations generated by a discrete-time strictly stationary ergodic process.
Salim Bouzebda, Sultana Didi
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Inverse-Probability-Weighted Kernel Estimation of Regression Derivatives Under Missing-at-Random Responses for Stationary Ergodic Processes [PDF]
EntropyThis paper develops asymptotic theory for kernel estimation of density-weighted conditional functionals and regression derivatives when responses are missing at random (MAR) and the observations form a strictly stationary ergodic process.
Salim Bouzebda, Sultana Didi
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On using a non-probability sample for the estimation of population parameters
Lietuvos Matematikos Rinkinys, 2023 We aim to find a way to effectively integrate a non-probability (voluntary) sample under the data framework, where the study variable is also observed in a probability sample of some statistical survey.
Ieva Burakauskaitė, Andrius Čiginas
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International Journal of Turkish Education Sciences, 2023 In this Monte Carlo simulation study, the performance of six different propensity score methods implemented through weighting cases was investigated: inverse probability of treatment weighting, truncated inverse probability of treatment weighting ...
Sungur Gürel, Walter Lana Leite
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Stats, 2021 Without randomization of treatments, valid inference of treatment effects from observational studies requires controlling for all confounders because the treated subjects generally differ systematically from the control subjects.
Tingting Zhou +2 more
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Health Technology Assessment, 2021 Background: Although routine NHS data potentially include all patients, confounding limits their use for causal inference. Methods to minimise confounding in observational studies of implantable devices are required to enable the evaluation of patients ...
Albert Prats-Uribe +17 more
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Frontiers in Oncology, 2022 PrecisThe exclusion of unmatched observations in propensity score matching has implications for the generalizability of causal effects. Machine learning methods can help to identify how the study population differs from the unmatched subpopulation ...
Debashis Ghosh +3 more
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World Journal of Surgical Oncology, 2023 Background Cholecystitis can represent a comorbidity during gallbladder cancer surgery; nonetheless, the prognostic impact of acute cholecystitis comorbidity remains unclear.
Yasuhiro Kihara +2 more
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Mathematics, 2020 Online surveys are increasingly common in social and health studies, as they provide fast and inexpensive results in comparison to traditional ones. However, these surveys often work with biased samples, as the data collection is often non-probabilistic ...
Luis Castro-Martín +2 more
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The use of propensity score-matching methods in evaluation of active labour market programs in Serbia [PDF]
Ekonomski Anali, 2007 The aim of the paper is to evaluate impacts of active labour market measures in Serbia on the probability of the participants’ employment. In the estimation process of average treatment effects, propensity score-matching method was applied.
Ognjenović Kosovka
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