Hasil untuk "stat.CO"

Jan de Leeuw

We present R and C implementations for metric (ratio) and non-metric (ordinal) versions of Elastic MDS, the multidimensional scaling technique proposed by McGee (1966). The R and C versions are compared for speed, with the C version anywhere from 15 to 100 times as fast as the R version.

Connor Panish, Leo Villani

We present a concise survey of matrix completion methods and associated implementations of several fundamental algorithms. Our study covers both passive and adaptive strategies. We further illustrate the behavior of a simple adaptive sampling scheme through controlled synthetic experiments.

Jun Xian, Xiaoda Xu

We study the expected $ L_2-$discrepancy under two classes of partitions, explicit and exact formulas are derived respectively. These results attain better expected $L_2-$discrepancy formulas than jittered sampling.

Robert D. Skeel, Carsten Hartmann

This article considers the application of Langevin dynamics to sampling and investigates how to choose the damping parameter in Langevin dynamics for the purpose of maximizing thoroughness of sampling. Also, it considers the computation of measures of sampling thoroughness.

Remi Daviet

The paper proposes a new Monte-Carlo simulator combining the advantages of Sequential Monte Carlo simulators and Hamiltonian Monte Carlo simulators. The result is a method that is robust to multimodality and complex shapes to use for inference in presence of difficult likelihoods or target functions. Several examples are provided.

Timo Bechger, Gunter Maris, Maarten Marsman

This paper discusses a Metropolis-Hastings algorithm developed by \citeA{MarsmanIsing}. The algorithm is derived from first principles, and it is proven that the algorithm becomes more efficient with more data and meets the growing demands of large scale educational measurement.

Y. Fan, S. A. Sisson

This Chapter, "ABC Samplers", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and algorithms used to sample from the ABC approximation to the posterior distribution, including methods based on rejection/importance sampling, MCMC and sequential Monte Carlo.

Madeleine B. Thompson

Orthogonal decomposition of the square root of a probability density function in the Hermite basis is a useful low-dimensional parameterization of continuous probability distributions over the reals. This representation is formally similar to the representation of quantum mechanical states as wave functions, whose squared modulus is a probability density.

Jiwoong Kim

This article investigates a fast and stable method to solve Henderson's mixed model equation. The proposed algorithm is stable in that it avoids inverting a matrix of a large dimension and hence is free from the curse of dimensionality. This tactic is enabled through row operations performed on the design matrix.

Alexander Gribov

One of the most efficient ways to produce unconditional simulations is with the kernel convolution using fast Fourier transform (FFT) [1]. However, when data is located on a surface, this approach is not efficient because data needs to be processed in a three-dimensional enclosing box. This paper describes a novel approach based on integer transformation to reduce the volume of the enclosing box.

Hien D. Nguyen, Geoffrey J. McLachlan

A divide-and-conquer method for parameter estimation is the chunked-and-averaged (CA) estimator. CA estimators have been studied for univariate parameters under independent and identically distributed (IID) sampling. We study the CA estimators of vector parameters and under non-IID sampling.

Jiwoong Kim

This article provides a full description of the R package KoulMde which is designed for Koul's minimum distance estimation method. When we encounter estimation problems in the linear regression and autogressive models, this package provides more efficient estimators than other R packages.

Thomas House

A natural method for the introduction of second-order derivatives of the log likelihood into MCMC algorithms is introduced, based on Taylor expansion of the Langevin equation followed by exact solution of the truncated system.

Alexander Gribov

One of the most efficient ways to produce unconditional simulations is with the spectral method using fast Fourier transform (FFT) [1]. But this approach is not applicable to arbitrary surfaces because no regular grid exists. However, points on the arbitrary surface can be generated randomly using uniform distribution to replace a regular grid. This paper will describe a nonstationary kernel convolution approach for data on arbitrary surfaces.

Adelchi Azzalini, Giovanna Menardi

The R package pdfCluster performs cluster analysis based on a nonparametric estimate of the density of the observed variables. After summarizing the main aspects of the methodology, we describe the features and the usage of the package, and finally illustrate its working with the aid of two datasets.

Luai Al Labadi, Mahmoud Zarepour

In this paper, we develop simple, yet efficient, procedures for sampling approximations of the two-Parameter Poisson-Dirichlet Process and the normalized inverse-Gaussian process. We compare the efficiency of the new approximations to the corresponding stick-breaking approximations, in which we demonstrate a substantial improvement.

Jannis Dimitriadis

Extending recent work of Corrado, we derive an algorithm that computes rigorous upper and lower bounds for rectangle scan probabilities for Markov increments. We experimentally examine the closeness of the bounds computed by the algorithm and we examine the range of tractable input variables.

Xinjia Chen

In this paper, we develop an approach for optimizing the explicit binomial confidence interval recently derived by Chen et al. The optimization reduces conservativeness while guaranteeing prescribed coverage probability.

Mark A. Beaumont

This note describes the results of some tests of the ABC-PRC algorithm of Sissons et al. (PNAS, 2007), and demonstrates with a toy example that the method does not converge on the true posterior distribution.

Anastasia Papavasiliou

We consider multiscale stochastic systems that are partially observed at discrete points of the slow time scale. We introduce a particle filter that takes advantage of the multiscale structure of the system to efficiently approximate the optimal filter.