Tim Hesterberg

(2002). Monte Carlo Strategies in Scientific Computing. Technometrics: Vol. 44, No. 4, pp. 403-404.

A linear regression-based model for the calculation of short-term system load forecasts is described. The model's most significant aspects fall into the following areas: innovative model building, including accurate holiday modeling by using binary variables and temperature modeling by using heating and cooling degree functions; robust parameter estimation and parameter estimation under heteroskedasticity by using weighted least-squares linear regression techniques; use of 'reverse errors-in-variables' techniques to mitigate the effects on load forecasts of potential errors in the explanatory variables; and distinction between time-independent daily peak load forecasts and the maximum of the hourly load forecasts in order to prevent peak forecasts from being negatively biased. The model was tested under a wide variety of conditioning and is shown to produce excellent results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

-intervals for small samples, though more accurate for larger samples. My goals in this article are to provide a deeper understanding of bootstrap methods-how they work, when they work or not, and which methods work better-and to highlight pedagogical issues. Supplementary materials for this article are available online. [Received December 2014. Revised August 2015].

Importance sampling uses observations from one distribution to estimate for another distribution by weighting the observations. Including the target distribution as one component of a mixture distribution bounds the weights and makes importance sampling more reliable. The usual importance-sampling estimate is a weighted average with weights that do not sum to 1. We discuss simple normalization and other, more efficient normalization methods. These innovations make importance sampling useful in a wider variety of problems. We demonstrate with a case study of oil-inventory reliability at a large utility.

Abstract This article provides an introduction to the bootstrap. The bootstrap provides statistical inferences—standard error and bias estimates, confidence intervals, and hypothesis tests—without assumptions such as Normal distributions or equal variances. As such, bootstrap methods can be remarkably more accurate than classical inferences based on Normal or t distributions. The bootstrap uses the same basic procedure regardless of the statistic being calculated, without requiring the use of application‐specific formulae. This article may provide two big surprises for many readers. The first is that the bootstrap shows that common t confidence intervals are woefully inaccurate when populations are skewed, with one‐sided coverage levels off by factors of two or more, even for very large samples. The second is that the number of bootstrap samples required is much larger than generally realized. WIREs Comp Stat 2011 3 497–526 DOI: 10.1002/wics.182 This article is categorized under: Statistical and Graphical Methods of Data Analysis &gt; Bootstrap and Resampling