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- W1857242593 abstract "Linear regression studies the problem of estimating a model parameter $beta^* in mathbb{R}^p$, from $n$ observations ${(y_i,mathbf{x}_i)}_{i=1}^n$ from linear model $y_i = langle mathbf{x}_i,beta^* rangle + epsilon_i$. We consider a significant generalization in which the relationship between $langle mathbf{x}_i,beta^* rangle$ and $y_i$ is noisy, quantized to a single bit, potentially nonlinear, noninvertible, as well as unknown. This model is known as the single-index model in statistics, and, among other things, it represents a significant generalization of one-bit compressed sensing. We propose a novel spectral-based estimation procedure and show that we can recover $beta^*$ in settings (i.e., classes of link function $f$) where previous algorithms fail. In general, our algorithm requires only very mild restrictions on the (unknown) functional relationship between $y_i$ and $langle mathbf{x}_i,beta^* rangle$. We also consider the high dimensional setting where $beta^*$ is sparse ,and introduce a two-stage nonconvex framework that addresses estimation challenges in high dimensional regimes where $p gg n$. For a broad class of link functions between $langle mathbf{x}_i,beta^* rangle$ and $y_i$, we establish minimax lower bounds that demonstrate the optimality of our estimators in both the classical and high dimensional regimes." @default.
- W1857242593 created "2016-06-24" @default.
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- W1857242593 date "2015-05-13" @default.
- W1857242593 modified "2023-09-27" @default.
- W1857242593 title "Optimal linear estimation under unknown nonlinear transform" @default.
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