[Methods-l] Next Friday - Monte Carlo Sampling Error Colloquium

[CRMDA.]

Hello All,

This is a reminder that we will host a CRMDA Colloquium next Friday, February 9th from 3-4 p.m. in Watson Library room 455. This presentation will be over the Monte Carlo Sampling Error. Dr. Adam Hafdahl will discuss ongoing investigations of strategies to quantify and control sampling error in Monte Carlo estimates of point-estimator properties (PEPs) such as (relative) bias, variance, and (root) mean squared error.

Below is a copy of the full Abstract:
Monte Carlo simulation has been used extensively for decades to study statistical methods' performance under diverse conditions.
More recently, Monte Carlo techniques have begun spreading beyond quantitative methodologists to applied researchers via their
wider role in planning studies (e.g., power analysis) as well as simulation estimates from computationally intensive data analyses
(e.g., resampling, multiple imputation, Bayesian techniques).

Because Monte Carlo results are stochastic, their sampling error may be an important source of uncertainty, especially when
resource constraints permit relatively few replications.  Building on two previous CRMDA talks, in this presentation I discuss
ongoing investigations of strategies to quantify and control sampling error in Monte Carlo estimates of point-estimator properties
(PEPs) such as (relative) bias, variance, and (root) mean squared error.

After briefly reviewing the focal problem and delta-method techniques for inference about PEPs via an asymptotic variance,
I first report an updated simulation study of delta-method confidence intervals (CIs) for several common PEPs.  I then describe
two alternatives to delta-method CIs -- random partitions of replications and nonparametric bootstrap variants -- and report
preliminary simulation studies of their performance.

Finally, I sketch several concepts, procedures, and issues pertaining to three of many associated topics to explore
further: transforming PEP estimators to facilitate inference, planning the number of Monte Carlo replications, and more complex
estimands such as comparisons or contrasts of two or more PEPs.
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Registration for this colloquium is not required. We hope you can join us for our first colloquium of the Spring semester.

Thank you,


Auburn Packer
Administrative Associate
Center for Research Methods & Data Analysis
College of Liberal Arts & Sciences | University of Kansas
Watson Library, 470B
1425 Jayhawk Blvd | Lawrence, KS  66045
785.864.3353 | crmda.ku.edu | crmda at ku.edu<mailto:crmda at ku.edu> | auburnp at ku.edu<mailto:auburnp at ku.edu>





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