Article records
https://feeds.library.caltech.edu/people/Sherman-R-P/article.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenMon, 15 Apr 2024 14:16:55 +0000Proof of a fundamental result in self-similar traffic modeling
https://resolver.caltech.edu/CaltechAUTHORS:20161220-170756365
Authors: {'items': [{'id': 'Taqqu-M-S', 'name': {'family': 'Taqqu', 'given': 'Murad S.'}}, {'id': 'Willinger-W', 'name': {'family': 'Willinger', 'given': 'Walter'}}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert'}}]}
Year: 1997
DOI: 10.1145/263876.263879
We state and prove the following key mathematical result in self-similar traffic modeling: the superposition of many ON/OFF sources (also known as packet trains) with strictly alternating ON- and OFF-periods and whose ON-periods or OFF-periods exhibit the Noah Effect (i.e., have high variability or infinite variance) can produce aggregate network traffic that exhibits the Joseph Effect (i.e., is self-similar or long-range dependent). There is, moreover, a simple relation between the parameters describing the intensities of the Noah Effect (high variability) and the Joseph Effect (self-similarity). This provides a simple physical explanation for the presence of self-similar traffic patterns in modern high-speed network traffic that is consistent with traffic measurements at the source level. We illustrate how this mathematical result can be combined with modern high-performance computing capabilities to yield a simple and efficient linear-time algorithm for generating self-similar traffic traces.We also show how to obtain in the limit a Lévy stable motion, that is, a process with stationary and independent increments but with infinite variance marginals. While we have presently no empirical evidence that such a limit is consistent with measured network traffic, the result might prove relevant for some future networking scenarios.https://authors.library.caltech.edu/records/982f3-3v891Subject Acquisition for Web-Based Surveys
https://resolver.caltech.edu/CaltechAUTHORS:ALVpa03
Authors: {'items': [{'id': 'Alvarez-R-M', 'name': {'family': 'Alvarez', 'given': 'R. Michael'}, 'orcid': '0000-0002-8113-4451'}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert P.'}}, {'id': 'VanBeselaere-C', 'name': {'family': 'VanBeselaere', 'given': 'Clara'}}]}
Year: 2003
DOI: 10.1093/pan/11.1.23
This article provides a basic report about subject recruitment processes for Web-based surveys. Using data from our ongoing Internet Survey of American Opinion project, two different recruitment techniques (banner advertisement and subscription campaigns) are compared. This comparison, together with a typology of Web-based surveys, provides insight into the validity and generalizability of Internet survey data. The results from this analysis show that, although Internet survey respondents differ demographically from the American population, the relationships among variables are similar across recruitment methods and match those implied by substantive theory. Thus, our research documents the basic methodology of subject acquisition for Web-based surveys, which, as we argue in our conclusion, may soon become the survey interview mode of choice for social scientists.https://authors.library.caltech.edu/records/s0t8c-nd813An equivalence result for VC classes of sets
https://resolver.caltech.edu/CaltechAUTHORS:JOSet03
Authors: {'items': [{'id': 'Joslin-S', 'name': {'family': 'Joslin', 'given': 'Scott'}}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert P.'}}]}
Year: 2003
DOI: 10.1017/S0266466603196090
Let R and θ be infinite sets and let A # R × θ. We show that the class of projections of A onto R is a Vapnik–Chervonenkis (VC) class of sets if and only if the class of projections of A onto θ is a VC class. We illustrate the result in the context of semiparametric estimation of a transformation model. In this application, the VC property is hard to establish for the projection class of interest but easy to establish for the other projection class.https://authors.library.caltech.edu/records/7bbxr-1zf98Sharp bounds under contaminated or corrupted sampling with verification, with an application to environmental pollutant data
https://resolver.caltech.edu/CaltechAUTHORS:20200204-084737505
Authors: {'items': [{'id': 'Dominitz-J', 'name': {'family': 'Dominitz', 'given': 'Jeff'}}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert P.'}}]}
Year: 2004
DOI: 10.1198/108571104x3389
Let F denote a distribution of interest and G a possibly spurious distribution. This article derives and nonparametrically estimates sharp bounds on characteristics of F when the data are a mixture of F and G, and a fraction of the data is verified to be from F. Contaminated and corrupted mixtures, with and without monotonicity restrictions, are analyzed. The methods are particularly useful in analyzing environmental pollutant measurements obtained using gas chromatography-mass spectroscopy. Results are applied to measurements of organic pollutant concentrations from the Love Canal. We argue that a corruption with monotonic verification model may be the most appropriate model for this type of data.https://authors.library.caltech.edu/records/43k55-5re78Identifying Treatment Effects Under Data Combination
https://resolver.caltech.edu/CaltechAUTHORS:20140508-094952115
Authors: {'items': [{'id': 'Fan-Yanqin', 'name': {'family': 'Fan', 'given': 'Yanqin'}}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert'}}, {'id': 'Shum-M', 'name': {'family': 'Shum', 'given': 'Matthew'}, 'orcid': '0000-0002-6262-915X'}]}
Year: 2014
DOI: 10.3982/ECTA10601
We consider the identification of counterfactual distributions and treatment effects when the outcome variables and conditioning covariates are observed in separate data sets. Under the standard selection on observables assumption, the counterfactual distributions and treatment effect parameters are no longer point identified. However, applying the classical monotone rearrangement inequality, we derive sharp bounds on the counterfactual distributions and policy parameters of interest.https://authors.library.caltech.edu/records/7xate-rrx63Estimation and Inference in an Ecological Inference Model
https://resolver.caltech.edu/CaltechAUTHORS:20160329-102818531
Authors: {'items': [{'id': 'Fan-Yanqin', 'name': {'family': 'Fan', 'given': 'Yanqin'}}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert'}}, {'id': 'Shum-M', 'name': {'family': 'Shum', 'given': 'Matthew'}, 'orcid': '0000-0002-6262-915X'}]}
Year: 2015
DOI: 10.1515/jem-2015-0006
We interpret an ecological inference model as a treatment effects model in which the outcomes of interest and the conditional covariates come from separate datasets. In this setting, the counterfactual distributions and policy parameters of interest are only partially identified under a selection on observables assumption. In this paper, we provide estimation and inference procedures for structural prediction and counterfactual analysis in such models. We also illustrate the procedures with an application to US presidential elections.https://authors.library.caltech.edu/records/kjnh6-5tn83Identification and estimation in a correlated random coefficients binary response model
https://resolver.caltech.edu/CaltechAUTHORS:20150827-100807099
Authors: {'items': [{'id': 'Hoderlein-S', 'name': {'family': 'Hoderlein', 'given': 'Stefan'}}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert'}}]}
Year: 2015
DOI: 10.1016/j.jeconom.2015.03.044
We study a linear index binary response model with random coefficients BB allowed to be correlated with regressors X. We identify the mean of the distribution of B and show how the mean can be interpreted as a vector of expected relative effects. We use instruments and a control vector V to make X independent of B given V. This leads to a localize-then-average approach to both identification and estimation. We develop a √n-consistent and asymptotically normal estimator of a trimmed mean of the distribution of BB, explore its small sample performance through simulations, and present an application.https://authors.library.caltech.edu/records/bxmsf-1sp98Bounding Causal Effects in an Ecological Inference Problem: the Chilean Electoral Reform
https://resolver.caltech.edu/CaltechAUTHORS:20160329-112726192
Authors: {'items': [{'id': 'Corvalan-A', 'name': {'family': 'Corvalan', 'given': 'Alejandro'}}, {'id': 'Melo-E', 'name': {'family': 'Melo', 'given': 'Emerson'}, 'orcid': '0000-0002-8129-5239'}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert'}}, {'id': 'Shum-M', 'name': {'family': 'Shum', 'given': 'Matthew'}, 'orcid': '0000-0002-6262-915X'}]}
Year: 2016
This paper is concerned with making causal inferences with ecological data. Aggregate outcome information is combined with individual demographic information from separate data sources to make causal inferences about individual behavior. In addressing such problems, even under the selection on observables assumption often made in the treatment effects literature, it is not possible to identify causal effects of interest. However, recent results from the partial identification literature provide the tightest upper and lower bounds on these causal effects. We apply these bounds to data from Chilean mayoral elections that straddle a 2012 change in Chilean electoral law from compulsory to voluntary voting. Aggregate voting outcomes are combined with individual demographic information from separate data sources to determine the causal effect of the change in the law on voter turnout. The bounds analysis reveals that voluntary voting decreased expected voter turnout, and that other causal effects are overstated if the bounds analysis is ignored.https://authors.library.caltech.edu/records/066bs-y1683Nonparametric identification of the distribution of random coefficients in binary response static games of complete information
https://resolver.caltech.edu/CaltechAUTHORS:20180529-102940724
Authors: {'items': [{'id': 'Dunker-Fabian', 'name': {'family': 'Dunker', 'given': 'Fabian'}, 'orcid': '0000-0002-9465-7315'}, {'id': 'Hoderlein-Stefan', 'name': {'family': 'Hoderlein', 'given': 'Stefan'}}, {'id': 'Kaido-Hiroaki', 'name': {'family': 'Kaido', 'given': 'Hiroaki'}}, {'id': 'Sherman-R-P', 'name': {'family': 'Sherman', 'given': 'Robert'}}]}
Year: 2018
DOI: 10.1016/j.jeconom.2018.01.010
This paper studies binary response static games of complete information allowing complex heterogeneity through a random coefficients specification. The main result of the paper establishes nonparametric point identification of the joint density of all random coefficients except those on interaction effects. Under additional independence assumptions, we identify the joint density of the interaction coefficients. Moreover, we prove that in the presence of covariates that are common to both players, the player-specific coefficient densities are identified, while the joint density of all random coefficients is not point-identified. However, we do provide bounds on counterfactual probabilities that involve this joint density.https://authors.library.caltech.edu/records/hsdrf-sen70