Notice about Seminars at the Bureau of Labor Statistics

To be placed on the seminar attendance list at the Bureau of Labor Statistics (BLS), you need to email your name, affiliation and seminar name to wss_seminar@bls.gov (note that there is an underscore after wss') by noon at least two days in advance of the seminar or call 202-691-7524 and leave a message with this information.

BLS is located at 2 Massachusetts Avenue, NE. Washington, DC. The Visitor's Entrance is on 1st Street and is opposite Union Station. Take the Red Line to Union Station.

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Title: Design Effects for Unequal Weighting

• Organizer: Charles Day, WSS Methodology Program Chair
• Chair: Charles Day, WSS Methodology Program Chair
• Speaker Dr. Kim Henry, Statistics of Income Division, IRS
• Discussant: Vince Iannacchione, RTI International
• Date & Time: Tuesday, February 7, 12:30pm-2:00 pm
• Location: Bureau of Labor Statistics, Conference Center Room 10
  To be placed on the seminar attendance list at the Bureau of Labor Statistics you need to e-mail your name, affiliation, and seminar name to wss_seminar@bls.gov (underscore after 'wss') by noon at least 2 days in advance of the seminar or call 202-691-7524 and leave a message. Bring a photo ID to the seminar. BLS is located at 2 Massachusetts Avenue, NE. Use the Red Line to Union Station.
• Sponsor: WSS Methodology Program

Abstract:

Different approaches have been developed to summarize the impact of differential weighting in survey samples. The most popular measure is Kish's (1965, 1992) design-based design effect. Spencer (2000) proposed a simple model-based approach that depends on a single covariate to estimate the impact on variance of using variable weights. Both measures may not accurately produce design effects for unequal weighting induced by calibration adjustments. When the calibration covariates are correlated with the coverage/response mechanism, calibration weights can improve the MSE of an estimator. However, since calibration involves unit-level adjustments, in many applications it produces weights that are more variable than the base weights or weights from category-based nonresponse or postratification adjustments. The Kish and Spencer measures may not be appropriate here; an ideal measure of the impact of unequal calibration weights incorporates both the correlation between the survey variable and weights and the correlation between the survey variable and calibration covariates. We propose a model-based extension of the Spencer design-effect for different variables of interest in single-stage and cluster sampling and under calibration weight adjustments. The proposed methods are illustrated using complex sample case studies.

Title: Simulation-Based Maximum Likelihood Inference For Partially Observed Markov Process Models

• Speaker: Anindya Bhadra, Postdoctoral Fellow, Statistics, Texas A&M University
• Date: Tuesday, Feburary 7th, 2012
• Time: 10:00-11:00am
• Location: Monroe Hall, Room 451 (2115 G Street, NW, Washington, DC 20052) <
• Directions: Foggy Bottom-GWU Metro Stop on the Orange and Blue Lines. The campus map is at http://www.gwu.edu/explore/visitingcampus/campusmaps.
• Sponsor: The George Washington University, Department of Statistics. See http://departments.columbian.gwu.edu/statistics/academics/seminars for a list of seminars.

Abstract:

Estimation of static (or time constant) parameters in a general class of nonlinear, non-Gaussian, partially observed Markov process models is an active area of research. In recent years, simulation-based techniques have made estimation and inference feasible for these models and have offered great flexibility to the modeler. An advantageous feature of many of these techniques is that there is no requirement to evaluate the state transition density of the model, which is often high-dimensional and unavailable in closed-form. Instead, inference can proceed as long as one is able to simulate from the state transition density - often a much simpler problem. In this talk, we introduce a simulation-based maximum likelihood inference technique known as iterated filtering that uses an underlying sequential Monte Carlo (SMC) filter. We discuss some key theoretical properties of iterated filtering. In particular, we prove the convergence of the method and establish connections between iterated filtering and well-known stochastic approximation methods. We then use the iterated filtering technique to estimate parameters in a nonlinear, non-Gaussian mechanistic model of malaria transmission and answer scientific questions regarding the effect of climate factors on malaria epidemics in Northwest India. Motivated by the challenges encountered in modeling the malaria data, we conclude by proposing an improvement technique for SMC filters used in an off-line, iterative setting.

Title: Monotonicity in the Sample Size of the Length of Classical Confidence Intervals Abstract

• Speaker: Dr. Yaakov Malinovsky, Dept. of Math. and Stat., UMBC
• Date/Time: February 9, 2012, 3:30pm
• Location: Room 1313, Math Building, University of Maryland College Park (directions).
• Sponsor: University of Maryland, Statistics Program (seminar updates).

Abstract:

It is proved that the average length of standard confidence intervals for parameters of gamma and normal distributions monotonically decreases with the sample size. Though the monotonicity seems a very natural property, the proofs are based on fine properties of the classical gamma function and are of independent interest. (It is a joint work with Abram Kagan).

Title: Statistical Methods for Dynamic Models with Application Examples

• Speaker: Tao Lu, Department of Biostatistics and Computational Biology, University of Rochester, School of Medicine
• Date: Friday, Feburary 10th. 2012
• Time: 11:00-12:00 noon
• Location: Phillips Hall, Room 110 (801 22nd Street, NW, Washington, DC 20052)

Abstract:

A dynamical system in engineering and physics, specified by a set of differential equations, is usually used to describe a dynamic process which follows physical laws or engineering principles. The parameters in the dynamical system are usually assumed known. However, an interesting question to ask is how to estimate these parameters when they are not known before. In this talk, I show you two examples where various statistical methods are applied to dynamic models for estimating unknown parameters based on observed data. Eventually, we are interested in predicting the future behavior of the dynamic system. The first example is on modeling HIV viral load dynamics from a clinical trial study. The second is on modeling a complicated interactive network.

Title: Visual Clustering with Quantized Generalized Parallel Coordinates

• Speaker: Rida Moustafa, Ph.D., dMining-Technology (dMT)
• Time: 10:30 a.m. Refreshments, 10:45 a.m. Colloquium Talk
• Date: Friday, February 10, 2012
• Location: Research 1, Room 301, Fairfax Campus, George Mason University, 4400 University Drive, Fairfax, VA 22030
• Sponsor: George Mason University CDS/CCDS/Statistics Colloquium

Abstract:

Visual pattern discovery in large multivariate datasets is a challenging problem in the fields of data mining and exploratory data analysis. This is due, in part, to the visual cluttering problem, which depends on screen resolutions and the number of points. The cluttering defies most information visualization techniques in general and parallel coordinates in particular. The cluttering effect increases with the number of data records, which makes the visual detection of hidden clusters, trends, correlations, periodicity, and anomalies even more difficult.

In this talk we discuss our hybrid plots called the quantized generalized parallel coordinate plot (QGPCP). The QGPCP detects the frequency of the profile lines (or curves), which represent the multivariate observations in parallel coordinate space, and maps this frequency into a gray (or HSV) scale color to highlight the profile lines (or curves) in a crowded GPCP. The approach has shown a great success in mitigating cluttering and detecting clusters in very large data not only in parallel coordinates but also the Andrews plot and the scatterplot matrix. We demonstrate the QGPCP on cluster tracking and visualization on Remote sensing, Computer Network, and Housing data sets.

Title: Cancer As A Failure Of Multicellularity: The Role Of Cellular Evolution

• Speaker: John Pepper, Ph.D., Biometry Research Group, Division of Cancer Prevention, NCI
• Date/Time: Friday, February 24th 11:30am-12:30pm
• Location: Location: Executive Plaza North (EPN), Conference Room G, 6130 Executive Boulevard, Rockville MD. Photo ID and sign-in required.
• SMetro: Get off at the White Flint stop on the red line, and take Nicholson lane to Executive blvd. Make a Right and continue, crossing Old Georgetown rd. When the road bends to the right make a left turn to enter the executive plaza complex parking lot. EPN will be the right most of the two twin buildings
• Map: http://dceg.cancer.gov/images/localmap.gif
• Sponsor: Public Health and Biostatistics Section, WSS and the NCI

Abstract:

Cancer results froma process of cellular evolution. Key cancer defenses and vulnerabilities arose from the ancient evolutionary transition from single-celled to multicellular organisms. Because cellular evolution leads inexorably to cancer, organismal evolution has organized cell reproduction into patterns that are less subject to cellular evolution. We used an agent-based computational model of evolution inside tissues to test the hypothesis that cell differentiation is crucial to suppressing cellular evolution within the body. The hypothesis was supported. If this most basic safeguard is compromised, all the obstacles to cancer built by organismal evolution are quickly dismantled by cellular evolution within the organism. Other simulations addressed the origins of tissue invasion and metastasis