This study proposes multivariate kernel density estimation by stagewise minimization algorithm based on U-divergence and a simple dictionary. The dictionary consists of an appropriate scalar bandwidth matrix part the original data. resulting estimator brings us data-adaptive weighting parameters matrices, provides sparse representation estimation. We develop non-asymptotic error bound that we o...

This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM), describe its properties and give procedures to estimate it from data. For estimation of multimodal distributions we introduce the ExponentialBlurring-Mean-Shift nonparametric clustering algorithm. The experiments highlight the properti...

In a recent paper [2] we introduced the greedy Gradient Pursuit framework. This is a family of algorithms designed to find sparse solutions to underdetermined inverse problems. One particularly powerful member of this family is the (approximate) conjugate gradient pursuit algorithm, which was shown to be applicable to very large data sets and which was found to perform nearly as well as the tra...

The Lasso, the Forward Stagewise regression and the Lars are closely related procedures recently proposed for linear regression problems. Each of them can produce sparse models and can be used both for estimation and variable selection. In practical implementations these algorithms are typically tuned to achieve optimal prediction accuracy. We show that, when the prediction accuracy is used as ...

In this paper, we propose the Boosted Lasso (BLasso) algorithm that is able to produce an approximation to the complete regularization path for general Lasso problems. BLasso is derived as a coordinate descent method with a fixed small step size applied to the general Lasso loss function (L1 penalized convex loss). It consists of both a forward step and a backward step and uses differences of f...

Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO (l1-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has caught on rapidly, and sparked a great deal of research interest. In this paper, w...

In this paper we discuss statistical properties and rates of convergence of the Stochastic Dual Dynamic Programming (SDDP) method applied to multistage linear stochastic programming problems. We assume that the underline data process is stagewise independent and consider the framework where at first a random sample from the original (true) distribution is generated and consequently the SDDP alg...

The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions have been implemented. It is great and social importance to evaluate effects in COVID-19. With help a synthetic control method, regression discontinuity, state-space compartmental model, we evaluated treatment stagewise intervention policies. We found statistically significant broad string...

We consider the multistage stochastic programming problem where uncertainty enters the right-hand sides of the problem. Stochastic Dual Dynamic Programming (SDDP) is a popular method to solve such problems under the assumption that the random data process is stagewise independent. There exist two approaches to incorporate dependence into SDDP. One approach is to model the data process as an aut...