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- W2041215639 abstract "We consider the problem of estimating the location of an asymmetric cusp θ0 in a regression model. That means, we focus on regression functions, which are continuous at θ0, but the degree of smoothness from the left p0 could be different to the degree of smoothness from the right q0. The degrees of smoothness have to be estimated as well. We investigate the consistency with increasing sample size n of the least-squares estimates. It turns out that the rates of convergence of θˆn depend on the minimum b of p0 and q0 and that our estimator converges to a maximizer of a Gaussian process. In the regular case, that is, for b greater than 12, we have a rate of n and the asymptotic normality property. In the non-regular case, we have a representation of the limit distribution of θˆn as maximizer of a fractional Brownian motion with drift." @default.
- W2041215639 created "2016-06-24" @default.
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- W2041215639 date "2015-03-16" @default.
- W2041215639 modified "2023-09-26" @default.
- W2041215639 title "Asymmetric cusp estimation in regression models" @default.
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- W2041215639 doi "https://doi.org/10.1080/02331888.2015.1016027" @default.
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