No abstract

No abstract

The study showed a clear relationship between worsening socio-economic circumstances and depression.

Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data. This paper combines robust regression and sparse model estimation. A robust and sparse estimator is introduced by adding an L1 penalty on the coefficient estimates to the well-known least trimmed squares (LTS) estimator. The breakdown point of this sparse LTS estimator is derived, and a fast algorithm for its computation is proposed. In addition, the sparse LTS is applied to protein and gene expression data of the NCI-60 cancer cell panel. Both a simulation study and the real data application show that the sparse LTS has better prediction performance than its competitors in the presence of leverage points.

Thiti?|t tiih@* ThLM*i4t 6ht|c } UhLttULhhi*@|L? ?i|ih 4T*it ?Lh t 4T*i_ M)uhvhdufk kdv ehhq vxssruwhg e| dq D1U1F1 frqwudfw ri wkh Frppxqdxw h iudq fdlvh gh Ehojltxh dqg e| wkh Hxurshdq Frpplvvlrq xqghu wkh Wudlqlqj dqg Prelolw| ri Uhvhdufkhuv Surjudpph +Frqwudfw Qr HUEIPU[FW<;03546,