Debiasing the lasso: Optimal sample size for Gaussian designs

De-biasing the Lasso: Optimal Sample Size for Gaussian Designs

Performing statistical inference in high-dimensional models is an outstanding challenge. A major source of difficulty is the absence of precise information on the distribution of high-dimensional regularized estimators. Here, we consider linear regression in the high-dimensional regime p n and the Lasso estimator. In this context, we would like to perform inference on a high-dimensional paramet...

Optimal Non-Parametric Prediction Intervals for Order Statistics with Random Sample Size

‎ In many experiments, such as biology and quality control problems, sample size cannot always be considered as a constant value. Therefore, the problem of predicting future data when the sample size is an integer-valued random variable can be an important issue. This paper describes the prediction problem of future order statistics based on upper and lower records. Two different cases for the ...

Optimal Sample Size for Adword Auctions

Generalized Second Price (GSP) mechanism is widely used in ad auctions and reserve price is an effective tool for revenue maximization. The optimal reserve price depends on bidders’ value distribution, which, however, is generally unknown to auctioneers. A common practice for auctioneers is to first collect information about the value distribution by a sampling procedure and then apply the rese...

Optimal Sample Size for Multiple Testing :

Sample size determination for logistic regression

The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...