The lasso is a popular tool that can be used for variable selection and esti- mation, however, classical statistical inference cannot be applied for its estimates. In this thesis the classical and the group lasso is described together with effici- ent algorithms for the solution. The key part is dedicated to the post-selection inference for the lasso estimates where we explain why the classical inference is not suitable. Three post-selection tests for the lasso are described and one test is proposed also for the group lasso. The tests are compared in simulations where finite sample properties are examined. The tests are further applied on a practical example.
We establish estimation and model selection consistency, prediction and estimation bounds and persis...
This articles investigates the distribution of the solutions of the generalized linear lasso (GLL) s...
This thesis presents a detailed study of multinomial regression, with a special focus on its applica...
Recent developments by Lee et al. (2014) in post selection inference for the Lasso are adapted to th...
New version of our work with additional numerical experiments.This article investigates uncertainty ...
Making statistical inference on high-dimensional data has been an interesting topic in recent days. ...
Group lasso is a natural extension of lasso and selects variables in a grouped manner. However, grou...
In regression problems where covariates can be naturally grouped, the group Lasso is an attractive m...
Thesis (Ph.D.)--University of Washington, 2018The field of post-selection inference focuses on devel...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
The development of the classical inferential theory of mathematical statistics is based on the philo...
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in scie...
In the classical theory of statistical inference, data is assumed to be generated from a known model...
The Lasso of Tibshirani (1996) is a useful method for estimation and implicit selection of predictor...
Variable selection is an important property of shrinkage methods. The adaptive lasso is an oracle pr...
We establish estimation and model selection consistency, prediction and estimation bounds and persis...
This articles investigates the distribution of the solutions of the generalized linear lasso (GLL) s...
This thesis presents a detailed study of multinomial regression, with a special focus on its applica...
Recent developments by Lee et al. (2014) in post selection inference for the Lasso are adapted to th...
New version of our work with additional numerical experiments.This article investigates uncertainty ...
Making statistical inference on high-dimensional data has been an interesting topic in recent days. ...
Group lasso is a natural extension of lasso and selects variables in a grouped manner. However, grou...
In regression problems where covariates can be naturally grouped, the group Lasso is an attractive m...
Thesis (Ph.D.)--University of Washington, 2018The field of post-selection inference focuses on devel...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
The development of the classical inferential theory of mathematical statistics is based on the philo...
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in scie...
In the classical theory of statistical inference, data is assumed to be generated from a known model...
The Lasso of Tibshirani (1996) is a useful method for estimation and implicit selection of predictor...
Variable selection is an important property of shrinkage methods. The adaptive lasso is an oracle pr...
We establish estimation and model selection consistency, prediction and estimation bounds and persis...
This articles investigates the distribution of the solutions of the generalized linear lasso (GLL) s...
This thesis presents a detailed study of multinomial regression, with a special focus on its applica...