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Quantifying the impact of input estimation errors in data-driven stochastic simulation often encounters substantial computational challenges due to the entanglement of Monte Carlo and input data noises. In this paper, we propose a subsampling framework to ...

In stochastic simulation, input uncertainty refers to the output variability arising from the statistical noise in specifying the input models. This uncertainty can be measured by a variance contribution in the output, which, in the nonparametric setting, ...

As machine learning procedures become an increasingly popular modeling option among applied researchers, there has been a corresponding interest in developing valid tools for understanding their statistical properties and uncertainty. Tree-based ensembles ...

Signal denoising--also known as non-parametric regression--is often performed through shrinkage estimation in a transformed (e.g., wavelet) domain; shrinkage in the transformed domain corresponds to smoothing in the original domain. A key question in such ...

Markov Chain Monte Carlo (MCMC) rendering is extensively studied, yet it remains largely unused in practice. We propose solutions to several practicability issues, opening up path space MCMC to become an adaptive sampling framework around established ...

Two types of heuristic estimators based on Parzen kernels are presented. They are able to estimate the regression function in an incremental manner. The estimators apply two techniques commonly used in concept-drifting data streams, i.e., the ...

In Markov decision processes (MDPs), the variance of the reward-to-go is a natural measure of uncertainty about the long term performance of a policy, and is important in domains such as finance, resource allocation, and process control. Currently ...

Reliability testing of complex software at the system level is impossible due to the environmental constraint or the time limitation, so its reliability estimate is often obtained based on the reliability of subsystems or components. The ...

We propose a method for estimating the variance parameter of a discrete, stationary stochastic process that involves combining variance estimators at different run lengths using linear regression. We show that the estimator thus obtained is first-order ...

We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Our work builds on variance estimates for bagging proposed by Efron (1992, 2013) that are based on the ...

The replicated batch means RBM method for steady-state simulation output analysis generalizes both the independent replications IR and batch means BM methods. We analyze the performance of RBM in situations where the underlying stochastic process ...

We introduce a novel class of data cube, called latent-variable cube. For many data analysis tasks, data in a database can be represented as points in a multi-dimensional space. Ordinary data cubes compute aggregate functions over these "observed" data ...

Recently, the estimation of a population quantile has received quite attention. Existing quantile estimators generally assume that values of an auxiliary variable are known for the entire population, and most of them are defined under simple random ...

In single-particle reconstruction methods, projections of macromolecules at random orientations are collected. Often, several classes of conformations or binding states coexist in a biological sample, which requires classification, so that each ...

Most probability and statistics textbooks are loaded with dice, coins, and balls in urns. These are perfect metaphors for actual phenomena where uncertainty plays a role. However, students will greatly appreciate a real-life example. In this paper we ...

Capturing characteristics of large data streams has received considerable attention. The constraints in space and time restrict the data stream processing to only one pass (or a small number of passes). Processing data streams over sliding windows make ...

Independent replications (IR) and batch means (BM) are two of the most widely used variance-estimation methods for simulation output analysis. Alexopoulos and Goldsman conducted a thorough examination of IR and BM; and Andradóttir and Argon proposed the ...

Statistical tests have been employed to identify genes differentially expressed under different conditions using data from microarray experiments. The variance of gene expression levels is often required in various statistical tests; however, due to the ...

Call centres are becoming increasingly important in our modern commerce. We are interested in modelling the time-varying pattern of average customer service times at a bank call centre. Understanding such a pattern is essential for efficient operation ...

In this paper a novel stochastic image model in the transform domain is presented and its performance in image denoising application is experimentally validated. The proposed model exploits local subband image statistics and is based on geometrical ...

In this paper, an estimation of the Gaussian noise variance based on observed (measured) maximums of subsets of samples is given. Circumstances of the measurement environment being limited, only maximums of subsets of samples are available and the non-...