We develop the principal component analysis (PCA) approach to systematic liquidity measurement by introducing moving and expanding estimation windows. We evaluatethese methods along with traditional estimation techniques (full sample PCA and market average) in terms of ability to explain (1)...

Many applications in data analysis rely on the decomposition of a data matrix into a low-rank and a sparse component. Existing methods that tackle this task use the nuclear norm and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$\ell _1$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>ℓ</mi> <mn>1</mn> </msub> </math> </EquationSource> </InlineEquation>-cost functions as convex relaxations of the rank constraint and the sparsity measure,...</equationsource></equationsource></inlineequation>

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in...

We develop the principal component analysis (PCA) approach to systematic liquidity measurement by introducing moving and expanding estimation windows. We evaluatethese methods along with traditional estimation techniques (full sample PCA and market average) in terms of ability to explain (1)...