Subsampling estimates of the Lasso distribution.

More documents

Recommendations

Info

3.2 Limit in distribution 29 Figure 3.1: Monte Carlo estimates of the distribution of the root √ n( ˆβ j −β j ), j = 7, . . . , 10, with penalization parameter λ n = 2 √ n.
30 Application to the Lasso estimator
Page 1: ✄✄ ✄ ✄ ✄ ✄✄ ✄ ✄
Page 4 and 5: iv Abstract
Page 6 and 7: vi CONTENTS Contents Notation viii
Page 8 and 9: viii Notation List of Tables 6.1 Mo
Page 10 and 11: Chapter 1 Introduction In this thes
Page 12 and 13: Chapter 2 Minimizers of convex proc
Page 14 and 15: 2.1 Convergence in probability 5 fo
Page 16 and 17: 2.2 Convergence in distribution 7 a
Page 18 and 19: 2.2 Convergence in distribution 9 L
Page 20 and 21: 2.2 Convergence in distribution 11
Page 22 and 23: 2.2 Convergence in distribution 13
Page 24 and 25: 2.3 A continous mapping theorem for
Page 30 and 31: Chapter 3 Application to the Lasso
Page 32 and 33: 3.2 Limit in distribution 23 almost
Page 34 and 35: 3.2 Limit in distribution 25 We als
Page 36 and 37: 3.2 Limit in distribution 27 3.2.2
Page 40 and 41: Chapter 4 The adaptive Lasso in a h
Page 42 and 43: 33 B.3 (Adaptive irrepresentable co
Page 44 and 45: 35 exists a constant K d depending
Page 46 and 47: 4.1 Variable selection consistency
Page 48 and 49: 4.2 Partial asymptotic normality 39
Page 50 and 51: 4.3 Marginal regressors as initial
Page 52 and 53: Chapter 5 Subsampling In his semina
Page 54 and 55: 5.1 Pointwise consistency for distr
Page 56 and 57: 5.2 Uniform consistency for quantil
Page 64 and 65: Chapter 6 Numerical results Motivat
Page 66 and 67: 6.1 Low dimensinal setting 57 5. De
Page 68 and 69: 6.1 Low dimensinal setting 59 Figur
Page 70 and 71: 6.2 High dimensional setting 61 3.
Page 72 and 73: 6.2 High dimensional setting 63 (d)
Page 74 and 75: 6.2 High dimensional setting 65 Mod
Page 76 and 77: 6.2 High dimensional setting 67 Mod
Page 78 and 79: 6.2 High dimensional setting 69 Fig
Page 80 and 81: Chapter 7 Concluding remarks We rev
Page 82 and 83: Bibliography Andersen, P. and R. Gi
Page 84 and 85: Appendix A R Codes A.1 Simulation c
Page 86 and 87: A.1 Simulation code for the Lasso i
Page 88 and 89:
A.2 Simulation code for the adaptiv
Page 90 and 91:
A.2 Simulation code for the adaptiv
show all

Subsampling estimates of the Lasso distribution.

Create successful ePaper yourself

Delete template?

Save as template?