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.

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