Non‐convex penalized multitask regression using data depth‐based penalties
نویسندگان
چکیده
منابع مشابه
Outlier Detection Using Nonconvex Penalized Regression
This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the n data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual L1 penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The L1 penalty corresponds to ...
متن کاملPenalized Regression with Model-Based Penalties
Nonparametric regression techniques such as spline smoothing and local tting depend implicitly on a parametric model. For instance, the cubic smoothing spline estimate of a regression function based on observations ti; Yi is the minimizer of P(Yi (ti))2 + R ( 00)2. Since R ( 00)2 is zero when is a line, the cubic smoothing spline estimate favors the parametric model (t) = 0+ 1t: Here we conside...
متن کاملPenalized likelihood regression for generalized linear models with nonquadratic penalties
One popular method for fitting a regression function is regularization: minimize an objective function which enforces a roughness penalty in addition to coherence with the data. This is the case when formulating penalized likelihood regression for exponential families. Most smoothing methods employ quadratic penalties, leading to linear estimates, and are in general incapable of recovering disc...
متن کاملA Parallel Algorithm for Large-scale Nonconvex Penalized Quantile Regression
Penalized quantile regression (PQR) provides a useful tool for analyzing high-dimensional data with heterogeneity. However, its computation is challenging due to the nonsmoothness and (sometimes) the nonconvexity of the objective function. An iterative coordinate descent algorithm (QICD) was recently proposed to solve PQR with nonconvex penalty. The QICD significantly improves the computational...
متن کاملMulti-task Regression using Minimal Penalties Multi-task Regression using Minimal Penalties
In this paper we study the kernel multiple ridge regression framework, which we refer to as multi-task regression, using penalization techniques. The theoretical analysis of this problem shows that the key element appearing for an optimal calibration is the covariance matrix of the noise between the different tasks. We present a new algorithm to estimate this covariance matrix, based on the con...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Stat
سال: 2018
ISSN: 2049-1573,2049-1573
DOI: 10.1002/sta4.174