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Ecologists are interested in modeling the population growth of species in various ecosystems. Studying population dynamics can assist environmental managers in making better decisions for the environment. Traditionally, the sampling of…

Methodology · Statistics 2021-02-04 Rebecca E. Atanga , Edward L. Boone , Ryad A. Ghanam , Ben Stewart-Koster

In this paper, we determine which non-random sampling of fixed size gives the best linear predictor of the sum of a finite spatial population. We employ different multiscale superpopulation models and use the minimum mean-squared error as…

Statistics Theory · Mathematics 2007-06-13 Vinay J. Ribeiro , Rudolf H. Riedi , Richard G. Baraniuk

In this paper, we investigate the problem of assessing statistical methods and effectively summarizing results from simulations. Specifically, we consider problems of the type where multiple methods are compared on a reasonably large test…

Applications · Statistics 2015-10-07 Abigail Arnold , Jason Loeppky

We investigate the merits of replication, and provide methods for optimal design (including replicates), with the goal of obtaining globally accurate emulation of noisy computer simulation experiments. We first show that replication can be…

Methodology · Statistics 2019-01-28 Mickael Binois , Jiangeng Huang , Robert B Gramacy , Mike Ludkovski

We propose a framework for computing, optimizing and integrating with respect to a smooth marginal likelihood in statistical models that involve high-dimensional parameters/latent variables and continuous low-dimensional hyperparameters.…

Methodology · Statistics 2026-02-10 Omiros Papaspiliopoulos , Timothée Stumpf-Fétizon , Jonathan Weare

Starting with a set of weighted items, we want to create a generic sample of a certain size that we can later use to estimate the total weight of arbitrary subsets. For this purpose, we propose priority sampling which tested on Internet…

Data Structures and Algorithms · Computer Science 2007-05-23 Nick Duffield , Carsten Lund , Mikkel Thorup

In many high energy experiments, the physics quantities are obtained by measuring the cross sections at a few energy points over an energy region. This was referred to as scan experiment. The optimal design of the scan experiment (how many…

High Energy Physics - Phenomenology · Physics 2015-09-22 X. H. Mo

The aim of this article is to define a notion of cardinal utility function called measurable utility and to define it on a connected and separable subset of a weakly ordered topological space. The definition is equivalent to the ones given…

General Economics · Economics 2023-01-04 Gianmarco Caldini

Optimization software enables the solution of problems with millions of variables and associated parameters. These parameters are, however, often uncertain and represented with an analytical description of the parameter's distribution or…

Optimization and Control · Mathematics 2025-01-17 John R. Birge

The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of ``functional data analysis,'' it has often been assumed that a sample of random functions is observed…

Statistics Theory · Mathematics 2016-08-16 Peter Hall , Hans-Georg Müller , Jane-Ling Wang

In this chapter, we identify fundamental geometric structures that underlie the problems of sampling, optimisation, inference and adaptive decision-making. Based on this identification, we derive algorithms that exploit these geometric…

In processing large quantities of data, a fundamental problem is to obtain a summary which supports approximate query answering. Random sampling yields flexible summaries which naturally support subset-sum queries with unbiased estimators…

Data Structures and Algorithms · Computer Science 2011-02-28 Edith Cohen , Graham Cormode , Nick Duffield

Optimization of expensive computer models with the help of Gaussian process emulators in now commonplace. However, when several (competing) objectives are considered, choosing an appropriate sampling strategy remains an open question. We…

Optimization and Control · Mathematics 2013-10-03 Victor Picheny

We consider the problem of determining the optimal block (or subsample) size for a spatial subsampling method for spatial processes observed on regular grids. We derive expansions for the mean square error of the subsampling variance…

Statistics Theory · Mathematics 2007-06-13 Daniel J. Nordman , Soumendra N. Lahiri

We consider the problem of constructing optimal designs for population pharmacokinetics which use random effect models. It is common practice in the design of experiments in such studies to assume uncorrelated errors for each subject. In…

Applications · Statistics 2010-11-16 Holger Dette , Andrey Pepelyshev , Tim Holland-Letz

Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants bias their sampling using various…

We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts. Under a Bayesian framework, we formulate the optimal dynamic sampling decision as a…

Machine Learning · Statistics 2023-06-12 Gongbo Zhang , Sihua Chen , Kuihua Huang , Yijie Peng

Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous…

Applications · Statistics 2020-08-19 Trygve Olav Fossum , Cédric Travelletti , Jo Eidsvik , David Ginsbourger , Kanna Rajan

Dependency functions of dependent variables are relevant for i) performing uncertainty quantification and sensitivity analysis in presence of dependent variables and/or correlated variables, and ii) simulating random dependent variables. In…

Methodology · Statistics 2022-03-22 Matieyendou Lamboni

Microplastics contamination is one of the most rapidly growing research topics. However, monitoring microplastics contamination in the environment presents both logistical and statistical challenges, particularly when constrained resources…