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Single-index quantile regression (QR) models solve nonparametric dimensionality concerns. These models assume the answer is connected to one linear covariate combination. Like other QR models, single-index QR models can experience quantile crossover, resulting in erroneous response distribution. Recent literature has focused on this issue. We present single-index model noncrossing quantile curve methods and extend them to composite quantile regression in this research. We derive the estimators’ asymptotic properties and demonstrate their benefits using simulations and real data.
Faculti is a research, policy and scholarship streaming platform, set up by a former school teacher, that covers 8000 academics annually across 20 subjects, across the world. The aim is to interview academics and policy makers discussing their research or analysis without any journalistic influence or bias. More here
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