Purpose This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving accuracy of tourism demand nowcasting once monthly official statistical data, including historical visitor arrival macroeconomic variables, become available. Design/methodology/approach is first attempt use LASSO-MIDAS model proposed by Marsil...

Logistic models are studied as a tool to convert output from numerical weather forecasting systems (deterministic and ensemble) into probability forecasts for binary events. A logistic model obtains by putting the logarithmic odds ratio equal to a linear combination of the inputs. As any statistical model, logistic models will suffer from over-fitting if the number of inputs is comparable to th...

We introduce a simple, interpretable strategy for making predictions on test data when the features of the test data are available at the time of model fitting. Our proposal—customized training— clusters the data to find training points close to each test point and then fits an l1-regularized model (lasso) separately in each training cluster. This approach combines the local adaptivity of k-nea...

We consider the problem of estimating a sparse linear regression vector β∗ under a gaussian noise model, for the purpose of both prediction and model selection. We assume that prior knowledge is available on the sparsity pattern, namely the set of variables is partitioned into prescribed groups, only few of which are relevant in the estimation process. This group sparsity assumption suggests us...

1 Summary. The additive risk model is a useful alternative to the proportional hazards model. It postulates that the hazard function is the sum of the baseline hazard function and the regression function of covariates. In this article, we investigate estimation in the additive risk model with right censored survival data and high dimensional covariates. A LASSO (least absolute shrinkage and sel...

The L1 regularization such as the lasso has been widely used in regression analysis since it tends to produce some coefficients that are exactly zero, which leads to variable selection. We consider the problem of variable selection for factor analysis models via the L1 regularization procedure. In order to select variables each of which is controlled by multiple parameters, we treat parameters ...

This paper studies the intrinsic connection between a generalized LASSO and a basic LASSO formulation. The former is the extended version of the latter by introducing a regularization matrix to the coefficients. We show that when the regularization matrix is even- or under-determined with full rank conditions, the generalized LASSO can be transformed into the LASSO form via the Lagrangian frame...

The lasso [19] and group lasso [23] are popular algorithms in the signal processing and statistics communities. In signal processing, these algorithms allow for efficient sparse approximations of arbitrary signals in overcomplete dictionaries. In statistics, they facilitate efficient variable selection and reliable regression under the linear model assumption. In both cases, there is now ample ...