The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samp...

We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our app...

We exhibit an approximate equivalence between the Lasso es-timator and Dantzig selector. For both methods we derive parallel oracle inequalities for the prediction risk in the general nonparamet-ric regression model, as well as bounds on the p estimation loss for 1 ≤ p ≤ 2 in the linear model when the number of variables can be much larger than the sample size.

We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in the large-sample limit. The asymptotic distributions are derived for both the case where the estimators are tuned to perform consistent model selection and for the case where the estimators are tuned to perform conservative model selection. Our findings complement those of Knight and Fu (2000) a...

We study the estimation error of constrained M -estimators, and derive explicit upper bounds on the expected estimation error determined by the Gaussian width of the constraint set. Both of the cases where the true parameter is on the boundary of the constraint set (matched constraint), and where the true parameter is strictly in the constraint set (mismatched constraint) are considered. For bo...

Penalized linear regression methods are used for the accurate prediction of new observations and to obtain interpretable models. The performance these depends on properties true coefficient vector. LASSO method is a penalized that can simultaneously perform shrinkage variable selection in continuous process. Depending structure dataset, different estimators have been proposed overcome problems ...

BACKGROUND Multivariable confounder adjustment in comparative studies of newly marketed drugs can be limited by small numbers of exposed patients and even fewer outcomes. Disease risk scores (DRSs) developed in historical comparator drug users before the new drug entered the market may improve adjustment. However, in a high dimensional data setting, empirical selection of hundreds of potential ...

The Financial Times Stock Exchange (FTSE) Bursa Malaysia KLCI Index is a key component in the development of Malaysia's economic growth and complexity terms identifying factors that have substantial impact on Malaysian stock market has always been contentious issue. In this study, macroeconomic exchange rate, interest gold price, consumer price index, money supply M1, M2, M3, industrial product...