Abstract: Machine learning models are commonly used in applications with an objective of prediction. The complicated algorithms of many of these models, however, make them difficult to interpret. Methods have been proposed to provide insight into these "black-box" models, but there is little research that focuses on the situation when functional data are used as model inputs.
Abstract: Generalized linear mixed models (GLMMs) are often used to analyze non-Gaussian data arising from different studies. In Bayesian GLMMs, the commonly used improper priors may yield undesirable improper posterior distributions. Here we consider the popular improper uniform prior to the regression coefficients and several proper or improper priors including the widely used gamma and power priors on the variance components of the random effects. We derive necessary and sufficient conditions for posterior propriety for Bayesian binomial and Poisson GLMMs.
The Midwest Big Data Summer School will held online on May 17-20, 2021.
Title: Deep learning and its application to remotely sensed data
Location: Zoom
John L Eltinge, Assistant Director for Research and Methodology, U.S. Census Bureau
Title: Six Crises One Dozen Opportunities in Public-Stewardship Statistics
Title: Managing Machine Learning Risk: Interpretability and Robustness
Location: Zoom
Statistical Emulation with Dimension Reduction for Complex Forward Models in Remote Sensing
Title: The Extremes of Interpretability in Machine Learning: Sparse Decision Trees, Scoring Systems and Interpretable Neural Networks
Location: Zoom
Speaker: Aaditya Ramdas, Carnegie Mellon University
Title: Estimating means of bounded random variables by betting
Title: Estimating a Covariance Function from Fragments of Functional Data
Lecturer with the Department of Economics, Mathematics & Statistics, Birkbeck, University of London
Title: Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data
Title: Valid inferential models and conformal prediction.
Location: Zoom
Dr. Hukum ChandraICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
Dr. Li-Chun ZhangUniversity of Southampton
The Next Census: What's New?
Title: The New Wave in Robot Grasping
Location: Zoom
Speaker: Laura Miller
Virus and Prion Research Unit, USDA-ARS-NADC
Title: Integrate structural analysis, isoform diversity, and interferon-inductive propensity of ACE2 to predict SARS-CoV-2 susceptibility in vertebrates
Additional details about each talk and speaker will be made available on the Baker Center website.
Speaker: Eve Wurtele
Department of Genetics, Development, and Cell Biology, Iowa State University
Title: The role of ancestry in COVID-19 infection
Additional details about each talk and speaker will be made available on the Baker Center website
Speaker: Walter Moss
Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University
Title: Finding and Folding Functional RNA in SARS-CoV-2
Additional details about each talk and speaker will be made available on the Baker Center website
University of Akron, Department of Statistics
Modeling Sleep Apnea Events Data from Pacifier Users for Improved Diagnosis of OSA
Speaker: Yumou Qiu
Department of Statistics, Iowa State University
Title: Comparing containment measures by epidemiological effects of COVID-19
Additional details about each talk and speaker will be made available on the Baker Center website
University of Alberta, Department of Mathematical and Statistical Science
M-estimation in Low-rank Matrix Factorization: a General Framework
Speaker: Dr. Lily Wang
Department of Statistics, Iowa State University
Title: Nowcasting and Forecasting COVID-19 in the United States
Additional details about each talk and speaker will be made available on the Baker Center website
Speaker: Dr. Volodymyr Melnykov
University of Alabama, Department of Information Systems, Statistics, and Management Science
On model-based clustering of skewed matrix and tensor data
Speaker: Dan Jacobson
Computational Systems Biology, Biosciences, Oak Ridge National Laboratory
Title: A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm
Additional details about each talk and speaker will be made available on the Baker Center website
Speaker: Dr. Karin Dorman
Iowa State University, Department of Statistics
Improving "*-Seq" -- Applications in Protein-Protein Interactions, Repertoire Sequencing, Allotetraploid Genotyping, and Clustering High-Dimensional Data
Speaker: Claus Kadelka
Department of Mathematics, Iowa State University
Title: A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies
Additional details about each talk and speaker will be made available on the Baker Center website.
Speaker: Dr. Arnab K Laha
Indian Institute of Management, Ahmedabad, India
Time Series Clustering and Classification using Topological Data Analysis with applications to Finance
Speaker: Dr. Abhirup Datta
Johns Hopkins University, Department of Biostatistics
Random forests for spatially or serially correlated data
Speaker: Dr. Emily Berg
Iowa State University, Department of Statistics
Methodology to Address Challenges in Small Area Estimation for Erosion
Speaker: Dr. Michael Kearns
University of Pennsylvania, Department of Computer and Information Science
The Ethical Algorithm
Speaker:
Dr. HaiYing Wang
University of Connecticut, Department of Statistics
Sampling based inference for logistic regression
Speaker:
Dr. Peter Sherman
ISU Department of Aerospace Engineering and Department of Statistics
The Exact Covariance Eigenstructure for AR(1) Processes
Speaker:
Jennifer Newman
Iowa State University, Department of Mathematics
StegoAppDB: a Steganography Apps Forensics Image Database
Mikael Kuusela (Department of Statistics and Data Science, Carnegie Mellon University)
Advances in Spatio-Temporal Analysis of Global Oceanographic Data from Argo Profiling Floats
William Q. Meeker
Department of Statistics
Center for Nondestructive Evaluation
Iowa State University
The Cautious Use of Bayesian Methods in Reliability Data Analyses
Shu Yang, NC State
Analysis of randomized clinical trials with integrated information from real world evidence studies
Peihua Qiu, U. Florida
Modeling and Monitoring Spatio-Temporal Disease Incidence Rate Data
Jiwei Zhao
State University of New York at Buffalo
Nonignorable Missingness Mechanism Model Can Be Ignored
Ranjan Maitra
Department of Statistics, Iowa State University
Kernel Nonparametric Overlap-based Syncytial Clustering
D. Sen
SAMSI
Monte Carlo algorithms on distributed architectures
Department Seminar
Peng Liu
Department of Statistics, Iowa State University
Feature Selection in Microbiome Studies
Department Seminar
Lily Wang
Department of Statistics, Iowa State University
Statistical Modeling and Inference for Next-Generation Functional Data Analysis
George Zyskind Memorial Lecture
Jane-Ling Wang
University of California, Davis
Functional Snippets: Another form of functional data
Department Seminar
Cindy Yu
Department of Statistics, Iowa State University
Semi-Parametric Quantile Regression Imputation for Missing Response and Covariates Subject to NMAR Nonresponse
A team of Iowa State University graduate students beat nearly 150 teams from 114 universities in 28 countries, bringing home the top prize from the 20th annual Data Mining Cup (2019). Representatives from the winning team will describe their project in this talk.
More on this can be found in the following links:
Department Seminar
Dan Nettleton
Department Chair
Department of Statistics, Iowa State University
The opening seminar of the semester serves as a “Welcome” event for Statistics students, staff, and faculty. We will introduce some new members of our Statistics community, present student awards, celebrate some successes of the past year, and announce some upcoming opportunities.
Refreshments at 3:45pm in Snedecor 2101.
It is our pleasure to invite your attendance and participation at the 2nd Midwest Statistical Machine Learning Colloquium, May 13th 2019 at Iowa State University.
Please follow the link below for more information:
Jing Qiu
University of Delaware
A Bayesian approach to identify genes with multiple expression patterns for paired RNA-seq data
Snedecor Memorial Lecture
Marie Davidian
J. Stuart Hunter Distinguished Professor of Statistics
North Carolina State University (NCSU)
The Right Treatment for the Right Patient at the Right Time: Precision Medicine Through Treatment Regimes, SMARTs, and Statistics
Xi Chen
Stern School of Business, New York University
Quantile Regression for big data with small memory
Ranjan Maitra
Iowa State University
Three-dimensional Radial Visualization of High-dimensional Continuous and Discrete Datasets
Jun Zhu
University of Wisconsin, Madison
Identification of clusters in space based on lattice data
Krishna Athreya
Iowa State University
What is Brownian motion?
Department Seminar
Greg Ridgeway
University of Pennsylvania, Statistics & Criminology
Racial Profiling, Bad Cops, and Police Shootings
Xiaoyue (Zoe) Cheng
Department of Mathematics
University of Nebraska at Omaha
Recognition of Crop Alignment for Unmanned Aerial Vehicle Imagery Data
Susan Vanderplan
Iowa State University
CoNNOR: A Convolutional Neural Network for Outsole Recognition
Jingshen Wang
University of Michigan
Inference on Treatment Effects after Model Selection
Karen Nielsen
University of Michigan
Statistical Tools for Exploring and Testing Features of Waveform Data
Zhengling Qi
University of North Carolina at Chapel Hill
Learning Optimal Individualized Decision Rules with Risk Control
Susan Vanderplas
Iowa State University
CoNNOR: A Convolutional Neural Network for Outsole Recognition
Xin (Henry) Zhang
Department of Statistics
Florida State University
Covariate-adjusted tensor classification in high-dimensions
Xueying Tang
Columbia University
Analysis of Large Multi-Relational Networks
Jon Williams
University of North Carolina, Chapel Hill
Non-penalized variable selection in high-dimensional settings via generalized fiducial inference
Lynna Chu
Asymptotically distribution-free change-point detection for multivariate and non-Euclidean data
Linjun Zhang
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
Amy Braverman
Principal Statistician
Jet Propulsion Laboratory
Uncertainty Quantication for NASA's Orbiting Carbon Observatory-2 Mission
Farzad Sabzikar
Department of Statistics
Iowa State University
Stochastic processes with semi-long range dependence
William Q. Meeker
Department of Statistics
Iowa State University
Applications of the Fractional Random Weight Bootstrap
Giles Hooker
Cornell University
Decision Trees and CLT's: Inference and Machine Learning
Huilin Li
New York University, School of Medicine
Statistical Challenges in the Analyses of Human Microbiome Data
Arka Ghosh
Department of Statistics
Iowa State University
Queueing networks in heavy traffic: History and some recent results
Babette Brumback
University of Florida
Model-Based Standardization Using an Outcome Model with Random Effects
Lily Wang
Department of Statistics
Iowa State University
Estimation and Inference for Image-on-Scalar Regression with Application to Imaging Genetics Studies
Kris De Brananter
Department of Statistics
Iowa State University
ABSTRACT:
Convergence Rates for Uniform Confidence Intervals Based on Local Polynomial Regression Estimators
We investigate the convergence rates of uniform bias corrected confidence intervals for a smooth curve using local polynomial regression for both the interior and boundary region.
Qing Li
Industrial & Manufacturing Systems Engr
Iowa State University
Change-Points Detection in the Recurrent-Event Context via Bayesian Inference
Max Morris, Petrutza Caragea, Mark Kaiser
Department of Statistics
Iowa State University
ABSTRACT:
Statistics Opening Seminar
This introductory "seminar" is our official "Welcome" event for new faculty and students in the Department. Professors Morris, Caragea and Kaiser will introduce new Statistics faculty members, present student awards, and describe plans for some professional development workshops for new and returning graduate students.
Irène Gijbels
Department of Mathematics and Leuven Statistics Research Centre, KU Leuven, Belgium
Title: “Nonparametric density and regression estimation in case of measurement errors: a review”
Gustavo Didier
Tulane University
On multidimensional scaling
Gustavo Didier, Tulane University
Speaker: Seho Park
Abstract:
Survey data integration using mass imputation
SPEAKER: Xiaojun Mao
ABSTRACT:
Matrix Completion under Low-Rank Missing Mechanism
Speaker: Yeon-Jung Seo
Abstract:
Selection and assessment of bivariate Markov random field models
Sixia Chen
The University of Oklahoma Heath Services Center
Pseudo-population bootstrap methods for imputed survey data
Speaker: Andrew Sage
ABSTRACT
A Robust Residual-Based Approach to Random Forest Regression
Speaker: Yet Nguyen
ABSTRACT:
RNA-seq Differential Expression Analysis for Repeated-measures Data
Richard Smith
Department of Statistics and Operations Research
University of North Carolina, Chapel Hill
INFLUENCE OF CLIMATE CHANGE ON EXTREME WEATHER EVENTS
Abolfazl Safikhani
Department of Statistics
Columbia University
Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to
allow for piecewise stationarity, where the model is allowed to change at given time points. In this talk, we propose a
three-stage procedure for consistent estimation of both structural change points and parameters of high-
Lan Xue
Department of Statistics
Oregon State University
Semi-parametric method for non-ignorable missing in longitudinal data using refreshment samples.
Missing data is one of the major methodological problems in longitudinal studies. It not only reduces the sample size, but also can result in biased estimation and inference. It is crucial to correctly understand the missing mechanism and appropriately incorporate it into the estimation and inference procedures.
Tom Loughin
Statistics and Actuarial Science
Simon Fraser University
Adaptively Pruned Random Forests Using Likelihood-Based Trees
Farzad Sabzikar
Department of Statistics
Iowa State University
Tempered processes: Theory and applications
Jan Hanning
University of North Carolina at Chapel Hill
Generalized fiducial Inference: A Review
DATE AND TIME: Friday, March 2, 2018, 2:10 p.m.
PLACE: Snedecor 1109
SPEAKER: Nicholas Clark
ABSTRACT:
Self-Exciting Spatio-Temporal Models for Count Data
Zongwu Cai
Department of Economics
University of Kansas
Models on Testing Predictability of Asset Returns
Testing predictability of asset returns is a cornerstone issue in modern asset pricing
and the related fields. It has been one of the hottest research topics in asset pricing
field in the recent two decades. In this talk, I will combine several of my own papers
(published papers and ongoing projects) on testing predictability of asset returns and
Somak Dutta
Department of Statistics
Iowa State University
A Bayesian screening method in ultra-high dimensional settings
Michael Price
Department of Statistics
Iowa State University
An Investigation of Actuarial Fair Crop Insurance Rates Using Partial Derivatives of Penalized Bivariate Tensor Product B-splines
A Special Galentine's Day Event!
"Plotting Your Date With ggplot2"
Who: Dr. Heike Hofmann
When: Tuesday, February 13th, 5:15pm
Where: Snedecor 3121
RSVP ASAP: meetup.com/R-Ladies-Ames
More Info Here:
William G. Meeker
Department of Statistics
Iowa State University
Statistical Intervals Vive La Differénce!
Mark Nieman
Department of Political Science
Iowa State University
Strategic Binary Choice Models with Partial Observability
Yumou Qiu
University of Nebraska Lincoln
Threshold selection for covariance learning
Zihuai He
Columbia University
Inference for statistical interactions under misspecified or high-dimentional main effects
Xiongtao Dai
University of California, Davis
Principal Component Analysis for Functional Data on Riemannian Manifolds and Spheres
Yang Ni
University of Texas at Austin
Integrative Directed Cyclic Graphical Models with Heterogeneous Samples
Andee Kaplan
Duke University
A Fast Sampler for Data Simulation from Spatial, and Other, Markov Random Fields
Kris De Brabanter
Iowa State University
Department of Statistics and Computer Science
Local polynomial regression with correlated errors and unknown correlation structure
Yimin Xiao
Michigan State University
Joint Estimation of Fractal Indices for Bivariate Gaussian Processes
Multivariate (or vector-valued) stochastic processes are important in probability, statistics and various scientific areas as stochastic models. In recent years, there has been increasing interest in investigating their statistical inference and prediction.
Daniel Sewell
University of Iowa
Simultaneous and temporal autoregressive network models
Subhajit Dutta
Indian Institute of Technology, Kanpur
Multi-scale Classification using Localized Spatial Depth
Tim Hesterberg
Google, Inc.
Statistics and Big Data at Google
Google lives on data. Search, Ads, YouTube, Maps, ... - they all live on data. I'll tell stories about how we use data, how we're always experimenting to make improvements (yes, this includes your searches), and how we adapt statistical ideas to do things that have never been done before.
BIO:
Bin Yu
University of California, Berkeley
Three principles of data science: predictability, stability, and computability
Alejandro Murua
University of Montreal
A Bayesian lasso functional model-based clustering model
We develop a flexible model for the analysis and clustering of complete or sparse time-course data.
Sanvesh Srivastava
University of Iowa
Wasserstein barycenter and its application in distributed Bayesian inference
Yehua Li
Iowa State University
Department of Statistics
Semiparametric Regression and Nonparametric Hypothesis Tests for Functional Data
Petrutza Caragea
Iowa State University
Department of Statistics
Modeling Space Weather Relationships using Multiscale Dynamic Linear Models
David Matteson
Department of Statistical Science
Cornell University
Sparse Identification and Estimation of High-Dimensional Vector AutoRegressive Moving Averages
Chinmay Hegde
Electrical and Computer Engineering
Iowa State University
Fast(er) Algorithms for Machine Learning in High Dimensions
Max Morris
Iowa State University Department of Statistics
Statistics Opening Seminar
This introductory "seminar" is our official "Welcome" event for new faculty and students in the Department. Professors Morris, Caragea and Kaiser will introduce new Statistics faculty members, present student awards, and describe plans for some professional development workshops for new and returning graduate students.
Refreshments at 3:45pm in Snedecor 2101.
Donna Spiegelman
Harvard University
Measurement error: from Fuller to the future
Mikyoung Jun
Texas A&M University
Covariance models for spatial data on a global scale
Steve Lund
National Institute of Standards and Technology
Assessing High Dimensional Evidence: Scores, Probability, and Scientific Validity
Danica Ommen
South Dakota State University
Approximate Solutions to the Forensic Identification of Source Problems
John Sall
JMP
From Big Data to Big Statistics
Now that we have Big Data we have new challenges. When big data is wide, there are too many things to look at. When we screen for just the big effects, we need to worry about selection bias. When we graph significance, we have the change scales. When you have tall data, everything is significant, but many effects are too small to care about. When you have holes and bumps all over your data, you need something automatic to adapt to them. All this needs to be resolved in a Big Statistics analytic work flow.
Marina Vannucci
Rice University
Bayesian Variable Selection Methods for Large-scale Genomic and Neuroimaging Data
There is now a huge literature on methods for variable selection that use spike-and-slab priors. Such methods, in particular, have been quite successful for applications in a variety of different fields.
High-throughput genomics and neuroscience are two of such examples.
Chris Saunders
South Dakota State University
Using Subsampling to Investigate the Dependency of Match Probabilities on the Size of Writing Samples
Jean Opsomer
Professor
Department of Statistics
Colorado State University
Survey estimation of domain means that respect natural orderings
Ranjan Maitra
Dept. of Statistics
Iowa State University
FAST Adaptive Smoothed Thresholding for Improved Activation Detection in Low-Signal fMRI
Naomi Kaplan,
Hebrew University, Israel
Statistical Methods for Evaluating Forensic Evidence: The Case of Shoe Prints
David Miller:
Integrated Statistics, Woods Hole, MA,
Centre for Research into Ecological and Environmental Modelling & School of Mathematics and Statistics, University of St Andrews, Scotland
Woods Hole Oceanographic Institution, Woods Hole, MA
Where the whale-things are: Distribution, detectability and availability modelling for cetacean populations.
Webscraping with tidyverse Packages in R
Soumik Sarkar
Assistant Professor
Department of Mechanical Engineering Iowa State University
Spatiotemporal Graphical Modeling for Complex Cyber-Physical Systems
Yumou Qiu
Asst. Professor
University of Nebraska-Lincoln
Lincoln, NE
Statistical Inference for Large Precision Matrices with Applications to Brain Connectivity
Jin Tian
Associate Professor
Computer Science
Iowa State University
Ames, Iowa
Inference with Selection Bias in Causal Graphical Models
Dabao Zhang
Professor
Department of Statistics
Purdue University
West Lafayette. Indiana
A Novel Approach to Reveal Whole Systems of Gene-Gene Regulations
Hongyuan Cao
Assistant Professor
Department of Statistics
University of Missouri
Columbia, Missouri
Change-point estimation: another look at multiple testing problems
We consider large scale multiple testing for data that have locally
clustered signals. With this structure, we apply techniques from
change-point analysis and propose a boundary detection algorithm so that
the clustering information can be utilised. Consequently the precision of
Lan Wang
Professor
Department of Statistics
University of Minnesota
Minneapolis, Minnesota
Estimation and Inference of Quantile Regression Under Biased Sampling
Lan Zhu
Associate Professor
Department of Statistics
Oklahoma State University
Stillwater, Oklahoma
A Powerful Statistical Method for Identifying Genes under Positive Selection from the Human Genome
Vivek Roy
Professor
Department of Statistics
Iowa State University
Ames, Iowa
Standard errors for generalized importance sampling estimators
Using the Random Forest to Estimate In-Game Win Probability
Bill Meeker
Distinguished Professor
Department of Statistics
Iowa State University
Ames, Iowa
Service Life Prediction of Field-Exposed Units Based on Laboratory Accelerated Degradation Test Data
Joe Guinness
Assistant Professor
Department of Statistics
North Carolina State University
Raleigh, North Carolina
Permutation Methods for Sharpening Gaussian Process Approximations
Zhengyuan Zhu
Professor
Department of Statistics
Iowa State University
Ames, Iowa
Non-Stationary Spatial Models: Some Theory and Applications
Quentin Brummet
Chief, Experiments and Innovation Branch
Center for Administrative Records Research and Applications
U.S. Census Bureau
Administrative Records Use at the US Census Bureau
Sharon Lohr
Vice President
Westat
Rockville, MD
The Essential Survey Statistician
Jarad Niemi
Assistant Professor
Department of Statistics
Iowa State University
Ames, Iowa
Fully Bayesian analysis of RNAseq data for gene expression heterosis detection
Xiaoke Zhang
Assistant Professor
Department of Statistics
University of Delaware
Newark, Delaware
https://sites.google.com/a/udel.edu/xkzhang/home
A Systematic Study on Weighing Schemes for Functional Data
Zhengjun Zhang
Professor
Department of Statistics
University of Wisconsin
Madison, Wisconsin
ATM: Autoregressive Tail-index Model for Financial Time Series
(Dept Seminar) Max Morris, Department of Statistics, Iowa State University, Ames
This introductory “seminar” is our official “Welcome” event for new students in the Department. Professors Morris, Caragea, and Kaiser will introduce the new Statistics students, present student awards, and present the results of last year’s survey on professional development workshops for graduate students.
This introductory “seminar” is our official “Welcome” event for new students in the Department. Professors Morris, Caragea, and Kaiser will introduce the new Statistics students, present student awards, and present the results of last year’s survey on professional development workshops for graduate students
In many areas of application, like medical sciences, variables of interest are not directly observable and may be measured only in the presence of contaminating errors. These cases are often referred as ``measurement error problems.'' Kernel deconvolution density estimation (KDDE) is an approach for handling such measurement errors, which consists of separating out and estimating the density of a target variable from observations blurred by additive errors. The method involves an adaptation of kernel density estimation using the Fourier inversion theorem.
| Date: | Friday, February 05 |
| Time: | 2:00 pm -- 3:00 pm |
| Place: | Snedecor 2102 |
| Speaker: | Ian Mouzon and Evan "Pete" Walsh, Department of Statistics, Iowa State University |
We are excited to announce the formation of a new working group, the Efficient Computing Workflow group.
07 December 4:10 pm, Snedecor 3105
Multilevel Functional Data Analysis: Modeling and Testing, (Dept Seminar) Yuhang Xu, Department of Statistics, Iowa State University, Ames
04 December 3:10 pm, Snedecor 2113
Goodness of fit tests for spatial Markov random fields, (Dept Seminar) Andee Kaplan, Department of Statistics, Iowa State University, Ames
16 November 4:10 pm, Snedecor 3105
Bayesian Screening for Group Differences in High-Throughput Data, (Dept Seminar) Eric Lock, Department of Biostatistics, University of Minnesota, Minneapolis
11 November 4:10 pm, Snedecor 1109
Graphing better Impulse Response Functions for discrete-time linear models, (Other) Gray Calhoun, Department of Economics, Iowa State University, Ames
02 November 4:10 pm, Snedecor 3105
A Ballooned Beta-Logistic Model with a Bioassay Application, (Dept Seminar) Nancy Flournoy, Department of Statistics, University of Missouri
26 October 4:10 pm, Snedecor 3105
Fractional Factorial Designs with Clear Two-Factor Interactions, (Dept Seminar) Huaiqing Wu, Department of Statistics, Iowa State University, Ames
19 October 4:10 pm, Snedecor 3105
Design and Analysis of Definitive Screening Experiments (Zyskind Memorial Lecture), (Dept Seminar) Christopher J. Nachtsheim, Frank A. Donaldson Chair of Operations Management, Carlson School of Management, University of Minnesota, Minneapolis
16 October 1:00 pm, Snedecor 2113
Experimental designs for multiple responses with different models, (Dept Seminar) Wilmina Marget, Department of Statistics, Iowa State University, Ames
13 October 7:30 pm, Durham 0171
Statisticians at Work: Inspiration, Aspiration, Ambition, (Dept Seminar) Jeff Wu, Coca-Cola Chair in Engineering Statistics, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta
12 October 4:10 pm, Morrill Hall 2019
A Fresh Look at Effect Aliasing and Interactions: Some New Wine in Old Bottle, (Dept Seminar) Jeff Wu, Coca-Cola Chair in Engineering Statistics, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta
| Date: | Thursday, October 09 |
| Time: | 3:30 pm -- 4:30 pm |
| Place: | 2624 Howe Hall |
| Speaker: | Barret Schloerke |
05 October 4:10 pm, Alliant Energy-Lee Auditorium, Howe Hall
Statistics in the Era of Big Data � Genetics and Genomics, (Dept Seminar) Robert Gentleman, 23andMe
| Date: | Friday, October 03 |
| Time: | 2:10 pm -- 4:00 pm |
| Place: | Heady 378C |
| Speaker: | Shan Yang and Hainan Huang |
Shan Yang will make concluding comments on Merton's derivation of the Black-Scholes formula.
| Date: | Friday, September 19 |
| Time: | 9:00 am -- 10:00 am |
| Place: | Sweeney 1123 |
| Speaker: | Jon Hobbs |
Abstract:
In the statistical modeling of an environmental process, it can be
useful to decompose the process into large-scale and small-scale
| Date: | Monday, September 15 |
| Time: | 3:10 pm -- 4:00 pm |
| Place: | Carver 298 |
| Speaker: | Nicholas Beyler |
Nicholas Beyler (presenting), Sarah Nusser, Wayne Fuller, Gregory Welk
Iowa State University
Abstract:
| Date: | Wednesday, April 02 |
| Time: | 4:10 pm -- 5:00 pm |
| Place: | Snedecor 3105 |
| Speaker: | Jacques Delince, European Commission Joint Research Centre, IPTS - Agriculture and Life Sciences in the Economy, Seville, Spain |
WAYNE FULLER LECTURE
| Date: | Friday, March 15 |
| Time: | 12:00 pm -- 1:00 pm |
| Place: | Snedecor 2113 |
| Speaker: | Michael Price, Department of Statistics, ISU |
There is increasing interest in the use of administrative data as input to the estimation process for survey data.
Total Survey Error
Abstract:The survey working group will continue to discuss total survey error. We will meet on Friday 2/22 at 12:00 in Sned 2113. |
| Date: | Friday, February 01 |
| Time: | 12:00 pm -- 1:00 pm |
| Place: | Snedecor 2113 |
| Speaker: |
| Date: | Wednesday, January 30 |
| Time: | 3:10 pm -- 4:00 pm |
| Place: | Snedecor 2113 |
| Speaker: | Carson Sievert, Department of Statistics, ISU |
Data-driven web applications are quickly gaining traction as a reliable vehicle for
| Date: | Wednesday, January 23 |
| Time: | 3:10 am -- 4:00 pm |
| Place: | 2113 Snedecor |
| Speaker: | Heike Hofmann, Department of Statistics, Iowa State U |
Welcome to Spring Semester Statistical Graphics Group Meeting
The advent of ultra-high throughput sequencing technology has made it possible to directly sequence large numbers of mRNA fragments, obtaining a direct measure of mRNA abundance. Restriction enzyme fragmentation reliably produces a single sequenced fragment per mRNA, providing a direct measure of digital gene expression from tag counts. However, genes in complex organisms may encode multiple mRNAs, called isoforms which result in different proteins. Since each restriction site can belong to multiple isoforms, the counts must be partitioned among the is
| Date: | Thursday, April 15 |
| Time: | 8:00 am -- 9:00 am |
| Place: | 2113 Snedecor |
| Speaker: | Angela Zuo, Department of Statistics, ISU |
| Date: | Monday, April 12 |
| Time: | 4:10 pm -- 5:00 pm |
| Place: | 3105 Snedecor |
| Speaker: | Dale Zimmerman, Dept. Statistics & Actuarial Science, U of IA, IA City |
| Date: | Monday, April 12 |
| Time: | 10:30 am -- 11:30 am |
| Place: | 2113 Snedecor |
| Speaker: | Ling Huang, Department of Statistics, Iowa State U |
A Comprehensive Analysis for Multiple Genomic Data Types via Bayesian Path Analysis, (Bioinformatics and Genetic Statistics) Steve Lund, Department of Statistics, Iowa State University
