- Referenced in 461 articles
- extends the basic ideas to several samples, semiparametric and smooth models. Significance and confidence intervals ... subsequent chapters deal with resampling methods appropriate for linear regression models, generalized linear models...
- Referenced in 441 articles
- program is to provide sophisticated methods in a friendly interface requiring very little statistical sophistication ... latent structure, and to search for linear feedback models--and in the ability to calculate ... about the true structure in the large sample limit, provided that structure and the sample...
- Referenced in 58 articles
- methods include one and c-sample problems, shape estimation and testing, linear regression and principal...
- Referenced in 47 articles
- similarities, differences, and complementarities of the two methods. In an example using U.S. telecommunications demand ... both in- and out-of-sample over the usual linear regression model and over some ... models like PcGets. Thus, both methods are useful components of the modern applied econometric automated...
- Referenced in 110 articles
- phase of these samples would yield a linear system). This paper develops a non-convex ... initialization obtained by means of a spectral method, and then refines this initial estimate...
- Referenced in 37 articles
- directly from regularly or irregularly time-domain sampled data, without requiring the determination ... direct time-domain identification of continuous-time linear models but also to provide the potential ... supports most of the time-domain methods developed over the last thirty years ... identifying linear dynamic continuous-time parametric models from measured input/output sampled data; it provides transfer...
- Referenced in 124 articles
- stochastic, derivative-free methods for numerical optimization of non-linear or non-convex continuous optimization ... evolution strategy, new candidate solutions are sampled according to a multivariate normal distribution ... covariance matrix adaptation (CMA) is a method to update the covariance matrix of this distribution...
- Referenced in 185 articles
- network is trained on batches of randomly sampled time and space points. The algorithm ... Deep Galerkin method (DGM)” since it is similar in spirit to Galerkin methods, with ... neural network instead of a linear combination of basis functions. In addition, we prove...
- Referenced in 72 articles
- spaces are highly complex and hence sampling methods play a key role. This paper provides ... data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized ... process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual...
- Referenced in 52 articles
- linear or nonlinear constrained optimization problems. The basic optimization algorithm is the Method of Feasible ... also be solved, and the conjugate direction method of Fletcher and Reeves is used ... used without special knowledge of optimization techniques. Sample problems are inc! luded to help...
- Referenced in 4 articles
- Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints ... method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized ... weights with arbitrary linear constraints...
- Referenced in 23 articles
- Gaussianity identifies the full structure of a linear acyclic model, that is, a causal ordering ... case with conventional methods. However, existing estimation methods are based on iterative search algorithms ... this paper, we propose a new direct method to estimate a causal ordering and connection ... Gaussianity. In contrast to the previous methods, our algorithm requires no algorithmic parameters...
- Referenced in 33 articles
- method, Peto’s method, and a variety of suitable generalized linear (mixed-effects) models ... multivariate/multilevel models that account for non-independent sampling errors and/or true effects...
- Referenced in 29 articles
- several bootstrapped replications of a given sample, then intersecting the supports of the Lasso bootstrap ... Bolasso, is compared favorably to other linear regression methods on synthetic data and datasets from...
- Referenced in 10 articles
- linear stability regions that pair with known explicit SSP Runge-Kutta methods. These methods ... then tested on sample problems to demonstrate the sharpness of the SSP coefficient...
- Referenced in 40 articles
- datasets can be viewed as a noisy sampling of an underlying space, and tools from ... power of PIs is compared against existing methods, showing significant performance gains. We explore ... vector-based machine learning tools, such as linear sparse support vector machines, which identify features...
- Referenced in 20 articles
- sampling solution procedures for multistage stochastic linear programmes on parallel MIMD architectures. Multistage stochastic linear ... future uncertainty. There are a variety of methods for solving the deterministic equivalent forms ... problems, including the simplex and interior-point methods and nested Benders decomposition, which decomposes ... original problem into a set of smaller linear programming problems and has recently been shown...
- Referenced in 6 articles
- method extends linear mixed models to functional data consisting of n curves sampled...
- Referenced in 8 articles
- goodness-of-fit tests for the functional linear model with scalar response. The test statistics ... false discovery rate method. The finite sample properties of the tests are illustrated ... simulation study for a variety of linear models, underlying processes, and alternatives. The software provided...
- Referenced in 10 articles
- proposed to solve some non linear PDEs. For each method, a framework is provided ... then simulate it out of the sample using the optimal commands previously calculated. Parallelization methods...