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Nonparametric estimation of common regressors for similar curve data. (English) Zbl 0817.62029
Summary: The paper is concerned with data from a collection of different, but related, regression curves \((m_ j)_{j=1, \dots, N}\), \(N \gg 1\). In statistical practice, analysis of such data is most frequently based on low-dimensional linear models. It is then assumed that each regression curve \(m_ j\) is a linear combination of a small number \(L \ll N\) of common functions \(g_ 1, \dots, g_ L\). For example, if all \(m_ j\)’s are straight lines, this holds with \(L = 2\), \(g_ 1 \equiv 1\) and \(g_ 2 (x) = x\).
In this paper the assumption of a prespecified model is dropped. A nonparametric method is presented which allows estimation of the smallest \(L\) and corresponding functions \(g_ 1, \dots, g_ L\) from the data. The procedure combines smoothing techniques with ideas related to principal component analysis. An asymptotic theory is presented which yields detailed insight into properties of the resulting estimators. An application to household expenditure data illustrates the approach.

MSC:
62G07 Density estimation
62G20 Asymptotic properties of nonparametric inference
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