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AAPI Heritage Month Celebration

Saturday, May 7, 2022 - 4:30pm

To celebrate Asian American and Pacific Islander (AAPI) Heritage Month in May, the Ames AAPI community and other participating organizations (listed below) will hold a celebration event at 4:30 pm on May 7th in the Sunroom of ISU’s Memorial Union.  The event will include opening remarks by Ames City Mayor John Haila and ISU Associate Provost for Faculty Dr. Read more about AAPI Heritage Month Celebration

Wen Zhou, Colorado State: Integrative Group Factor Model for Variable Clustering on Temporally Dependent Date: Optimality and Algorithm

Monday, April 18, 2022 - 11:00am

Presenter: Dr. Wen Zhou, Colorado State University

Time: 11:00 AM Central Time, Monday, April 18, 2022

Title: Integrative Group Factor Model for Variable Clustering on Temporally Dependent Date: Optimality and Algorithm Read more about Wen Zhou, Colorado State: Integrative Group Factor Model for Variable Clustering on Temporally Dependent Date: Optimality and Algorithm

PhD Seminar: Miranda Tilberg, "Confidence intervals for the utilization distribution overlap index (UDOI)"

Tuesday, April 12, 2022 - 3:00pm

Location: Snedecor 1109 (live stream available at https://iastate.zoom.us/j/91269172339?pwd=M2xMSUJxZkxxWm9FcEdhbTNhODJOdz09)

Presenter: Miranda Tilberg, PhD Candidate in Statistics

Title: Confidence intervals for the utilization distribution overlap index (UDOI) Read more about PhD Seminar: Miranda Tilberg, "Confidence intervals for the utilization distribution overlap index (UDOI)"

Leah R. Johnson, Virginia Tech: "Strategic vs Tactical Modeling Approaches to Predicting Mosquito-borne Disease in the Americas"

Monday, April 11, 2022 - 11:00am

Presenter: Dr. Leah R. Johnson, Virginia Tech University

Time: 11:00 AM Central Time, Monday, April 11, 2022

Title: Strategic vs Tactical Modeling Approaches to Predicting Mosquito-borne Disease in the Americas Read more about Leah R. Johnson, Virginia Tech: "Strategic vs Tactical Modeling Approaches to Predicting Mosquito-borne Disease in the Americas"

Lingzhou Xue, Penn State University: An Additive Graphical Model for Discrete Data

Monday, March 21, 2022 - 11:00am

Abstract: We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence. Additive conditional independence is a three-way statistical relation that shares similar properties with conditional independence by satisfying the semi-graphoid axioms. Based on this relation we build an additive graphical model for discrete variables that does not suffer from the restriction of a parametric model such as the Ising model. Read more about Lingzhou Xue, Penn State University: An Additive Graphical Model for Discrete Data

Yuhlong Lio, University of South Dakota: Robust Control Charts for Percentiles Based on Location-Scale Family of Distributions

Monday, March 7, 2022 - 11:00am

Abstract: Robust control charts for percentiles based on location-scale family of distributions are proposed. In the construction of control charts for percentiles, when the underlying distribution of the quality measurement is unknown, we study the problem of discriminating different possible candidate distributions in the location-scale family of distributions and obtain control charts for percentiles which are insensitive to model mis-specification. Read more about Yuhlong Lio, University of South Dakota: Robust Control Charts for Percentiles Based on Location-Scale Family of Distributions

Won Chang, University of Cincinnati: Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

Monday, February 28, 2022 - 11:00am

Abstract: Most organisms exhibit various endogenous oscillating behaviors which provide crucial information as to how the internal biochemical processes are connected and regulated. Understanding the molecular mechanisms behind these oscillators requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Read more about Won Chang, University of Cincinnati: Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

Seminar: Tracy Ke, Harvard

Monday, February 14, 2022 - 11:00am

Abstract: The probabilistic topic model imposes a low-rank structure on the expectation of the corpus matrix. Therefore, singular value decomposition (SVD) is a natural tool of dimension reduction. We propose an SVD-based method for estimating a topic model. Our method constructs an estimate of the topic matrix from only a few leading singular vectors of the corpus matrix, and has a great advantage in memory use and computational cost for large-scale corpora. Read more about Seminar: Tracy Ke, Harvard

Daniela Witten, University of Washington: Selective inference for trees

Monday, November 1, 2021 - 11:00am

Abstract: As datasets grow in size, the focus of data collection has increasingly shifted away from testing pre-specified hypotheses, and towards hypothesis generation. Researchers are often interested in performing an exploratory data analysis to generate hypotheses, and then testing those hypotheses on the same data. Unfortunately, this type of 'double dipping' can lead to highly-inflated Type 1 errors. In this talk, I will consider double-dipping on trees. Read more about Daniela Witten, University of Washington: Selective inference for trees

Jae-Kwang Kim, Iowa State University, Weight model approach to Bayesian inference under informative sampling

Monday, October 25, 2021 - 11:00am

Abstract: In probability sampling,   the first-order inclusion probabilities are available for each unit in the sample. If the first-order inclusion probability is correlated with the study variable at hand, even after adjusting for the covariates in the model, then the sampling design becomes informative and the naive analysis ignoring the sampling design can lead to biased estimation. How to handle informative sampling for analytic inference with survey data is an important practical problem. Read more about Jae-Kwang Kim, Iowa State University, Weight model approach to Bayesian inference under informative sampling

STATers Tailgate

Saturday, October 23, 2021 - 11:30am

On October 23rd for the football game vs. Oklahoma State, STATers will be hosting a tailgate party in the parking lot of Jack Trice Stadium. There will be two spaces reserved where they will be grilling hotdogs/hamburgers and watching football! The tailgating event starts at 11:30 and the game starts at 2:30pm. Read more about STATers Tailgate

Derrick Rollins, Iowa State University: Dynamic Regression – Proposing a New Modeling Framework in Regression When Response Behavior is Not Static but Dynamic

Monday, October 18, 2021 - 11:00am

Presenter: Derrick Rollins, Iowa State University

Title: Dynamic Regression – Proposing a New Modeling Framework in Regression When Response Behavior is Not Static but Dynamic Read more about Derrick Rollins, Iowa State University: Dynamic Regression – Proposing a New Modeling Framework in Regression When Response Behavior is Not Static but Dynamic

Fall 2021 Welcome Seminar

Monday, August 23, 2021 - 11:00am to 11:50am

You are invited to the Fall 2021 Welcome Seminar for the Department of Statistics.  The seminar will be presented from 11:00-11:50 A.M. on Monday, August 23. The event will highlight some departmental accomplishments from the past year, discuss future events, present student awards, and introduce new students, faculty, and staff.

You can join via Zoom or if you prefer to attend the seminar in person may join in 0198 Parks Library, where face masks are encouraged. Read more about Fall 2021 Welcome Seminar

Seminar: Resolving Real Biological Sequences with Accurate Abundance Estimation from Noisy Illumina Amplicon Data

Thursday, August 19, 2021 - 1:00pm

Abstract: Amplicon sequencing has been widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and accurately quantifying their abundance from noisy amplicon sequence data is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, along with errors generated during Polymerase Chain Reaction (PCR) and sequencing. Read more about Seminar: Resolving Real Biological Sequences with Accurate Abundance Estimation from Noisy Illumina Amplicon Data

Seminar: Functional ANOVA-type methods with interpretable visualization for comparisons among groups of time series

Thursday, August 19, 2021 - 9:00am

Abstract: Data sampled densely in space and time have become increasingly abundant as a result of advances in modern technology. However, the presence of complex dependence and current computational limitations have made many classical inferential approaches practically infeasible. Read more about Seminar: Functional ANOVA-type methods with interpretable visualization for comparisons among groups of time series

Seminar: Exploratory Analysis of High-Dimensional Data with Visual Tools

Tuesday, August 17, 2021 - 12:00pm

Abstract: Exploratory data analysis and dimension reduction techniques serve as the backbone for developing visual tools designed to foster new ideas and an understanding of the underlying phenomenon of interest. The tour is a dynamic visualization technique for exploratory data analyses of high-dimensional numeric data consisting of a smooth sequence of d-dimensional projections from p-dimensional Euclidean space. Read more about Seminar: Exploratory Analysis of High-Dimensional Data with Visual Tools

Principal Component Analysis of Discrete Datasets

Thursday, August 12, 2021 - 2:00pm

Abstract:  We propose a Gaussian copula based method to perform principal component analysis for discrete data. By assuming the data are from a discrete distributions in the Gaussian copula family, we can consider the discrete random vectors are generated from a latent multivariate normal random vector. So we first obtain an estimate of the correlation matrix of latent multivariate normal distribution, then we use the estimated latent correlation matrix to get the estimates of principal components. Read more about Principal Component Analysis of Discrete Datasets

Seminar: Statistical Inference for Mean Functions of 3D Functional Objects

Wednesday, July 14, 2021 - 9:00am

Abstract: Functional data analysis has become a powerful tool for conducting statistical analysis for complex objects, such as curves, images, shapes, and manifold-valued data. Among these data objects, 2D or 3D images obtained using medical imaging technologies have been attracting researchers' attention. Examples are functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), which provide a very detailed characterization of brain activity. Read more about Seminar: Statistical Inference for Mean Functions of 3D Functional Objects

Seminar: A RKHS Approach for Variable Selection in High Dimensional Functional Linear Models

Tuesday, July 13, 2021 - 2:10pm

Abstract: There has been a recent surge in applications of high-dimensional functional data analysis. We explore functional linear regression by focusing on the large-scale scenario that scalar response is associated with potentially an ultra-large number of functional predictors in the setting of the reproducing kernel Hilbert space (RKHS) framework.  Read more about Seminar: A RKHS Approach for Variable Selection in High Dimensional Functional Linear Models

Seminar: Estimation and Inference of Quantile Spatially Varying Coefficient Models over Complicated Domains

Friday, July 9, 2021 - 8:00am

Abstract: Regression analysis is frequently used in the analyses of spatial data. In this paper, we propose a flexible quantile spatially varying coefficient model to assess how conditional quantiles of the response depend on covariates, allowing the coefficient function to vary with the spatial locations. The model can be used to explore spatial non-stationarity of a regression relationship for heterogeneous spatial data distributed over a domain of a complex or irregular shape. Read more about Seminar: Estimation and Inference of Quantile Spatially Varying Coefficient Models over Complicated Domains

Seminar: Estimation for nearest neighbor imputed survey data

Wednesday, July 7, 2021 - 10:10am

Abstract:  Sample surveys usually have missing data and have multiple response variables. Nearest neighbor imputation is one procedure used to complete missing records. However, the direct nearest neighbor imputation estimator suffers from a bias that increases as the dimension of the covariate vector increases. In this paper, we construct a regression estimator for the population mean based on the nearest neighbor imputed dataset. We use the regression procedure to reduce the bias in the direct nearest neighbor estimator. Read more about Seminar: Estimation for nearest neighbor imputed survey data

Seminar: estimation and inference of quantile spatially varying coefficient models over complicated domains

Tuesday, July 6, 2021 - 8:00am

Abstract: Regression analysis is frequently used in the analyses of spatial data. In this paper, we propose a flexible quantile spatially varying coefficient model to assess how conditional quantiles of the response depend on covariates, allowing the coefficient function to vary with the spatial locations. The model can be used to explore spatial non-stationarity of a regression relationship for heterogeneous spatial data distributed over a domain of a complex or irregular shape. Read more about Seminar: estimation and inference of quantile spatially varying coefficient models over complicated domains

Seminar: Bayesian Variable Selection for Spatial Linear Mixed Models in Ultra-high Dimensional Settings

Thursday, July 1, 2021 - 9:00am

Abstract: In agricultural field trial experiments, observations are naturally spatially auto-correlated. Variable selection for these types of data is typically performed using a two-stage procedure by first obtaining spatially adjusted effects and then applying a feature selection method developed for independent observations. Read more about Seminar: Bayesian Variable Selection for Spatial Linear Mixed Models in Ultra-high Dimensional Settings

Seminar: An Explainable Pipeline for Machine Learning with Functional Data

Monday, June 21, 2021 - 12:00pm

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. Read more about Seminar: An Explainable Pipeline for Machine Learning with Functional Data

Seminar: necessary and sufficient conditions for posterior propriety for generalized linear mixed models

Thursday, June 17, 2021 - 2:00pm

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. Read more about Seminar: necessary and sufficient conditions for posterior propriety for generalized linear mixed models

Theoretical and Applied Data Science Seminar - The Extremes of Interpretability in Machine Learning: Sparse Decision Trees, Scoring Systems and Interpretable Neural Networks, Dr. Cynthia Rudin

Thursday, April 8, 2021 - 12:00pm to 1:00pm

Cynthia Rudin headshotTitle: The Extremes of Interpretability in Machine Learning: Sparse Decision Trees, Scoring Systems and Interpretable Neural Networks

Location: Zoom Read more about Theoretical and Applied Data Science Seminar - The Extremes of Interpretability in Machine Learning: Sparse Decision Trees, Scoring Systems and Interpretable Neural Networks, Dr. Cynthia Rudin

Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data - Georgios Papageorgiou

Monday, March 29, 2021 - 11:00am to 12:00pm

Georgios Papageorgiou 

Lecturer with the Department of Economics, Mathematics & Statistics, Birkbeck, University of London

Title: Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data Read more about Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data - Georgios Papageorgiou

Baker Center Seminar Series on SARS-CoV2/COVID-19 - Laura Miller

Tuesday, December 1, 2020 - 10:00am to 11:00am

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.  Read more about Baker Center Seminar Series on SARS-CoV2/COVID-19 - Laura Miller

Baker Center Seminar Series on SARS-CoV2/COVID-19 - Dan Jacobson

Tuesday, October 20, 2020 - 10:00am to 11:00am

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

  Read more about Baker Center Seminar Series on SARS-CoV2/COVID-19 - Dan Jacobson

Improving "*-Seq" -- Applications in Protein-Protein Interactions, Repertoire Sequencing, Allotetraploid Genotyping, and Clustering High-Dimensional Data - Karin Dorman

Monday, October 19, 2020 - 11:00am

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 Read more about Improving "*-Seq" -- Applications in Protein-Protein Interactions, Repertoire Sequencing, Allotetraploid Genotyping, and Clustering High-Dimensional Data - Karin Dorman

Dept. Seminar - ISU Data Mining Cup Project

Monday, September 9, 2019 - 4:10pm

ISU Data Mining Cup project
Qihao Zhang, Yifan Zhu, Xingche Guo (Stat DMC Team)

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:

https://www.news.iastate.edu/news/2019/07/08/data-mining Read more about Dept. Seminar - ISU Data Mining Cup Project

Welcome Back Seminar - Dan Nettleton

Monday, August 26, 2019 - 4:10pm

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. Read more about Welcome Back Seminar - Dan Nettleton

The Right Treatment for the Right Patient at the Right Time: Precision Medicine Through Treatment Regimes, SMARTs, and Statistics

Monday, April 15, 2019 - 4:10pm

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 Read more about The Right Treatment for the Right Patient at the Right Time: Precision Medicine Through Treatment Regimes, SMARTs, and Statistics

Dept. Seminar - Kris De Brabanter

Monday, September 17, 2018 - 4:10pm

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. Read more about Dept. Seminar - Kris De Brabanter

Statistics Opening Seminar

Monday, August 27, 2018 - 4:10pm to 5:00pm

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.

  Read more about Statistics Opening Seminar

Dept. Seminar - Abolfazl Safikhani

Monday, April 9, 2018 - 4:10pm

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- Read more about Dept. Seminar - Abolfazl Safikhani

Dept. Seminar - Lan Xue

Monday, April 2, 2018 - 4:10pm to 5:00pm

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.  Read more about Dept. Seminar - Lan Xue

Dept. Seminar - Zongwu Cai

Monday, February 26, 2018 - 4:10pm to 5:00pm

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 Read more about Dept. Seminar - Zongwu Cai

Dept. Seminar - Yimin Xiao

Monday, November 27, 2017 - 4:10pm to 5:00pm

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.  Read more about Dept. Seminar - Yimin Xiao

Statistics Seminar Series on Data Science - Tim Hesterberg

Tuesday, October 31, 2017 - 11:00am to 11:50am

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:

Read more about Statistics Seminar Series on Data Science - Tim Hesterberg

Dept. Seminar - Max Morris

Monday, August 28, 2017 - 4:10pm to 5:00pm

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. Read more about Dept. Seminar - Max Morris

Dept. Seminar - John Sall, JMP

Tuesday, April 4, 2017 - 2:10pm to 3:00pm

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.  Read more about Dept. Seminar - John Sall, JMP

Dept. Seminar - Marina Vannucci

Monday, March 27, 2017 - 4:10pm to 5:00pm

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.  Read more about Dept. Seminar - Marina Vannucci

Dept. Seminar - David Miller

Monday, February 13, 2017 - 4:10pm to 5:00pm

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. Read more about Dept. Seminar - David Miller

Hongyuan Cao, Dept. Seminar

Monday, November 28, 2016 - 4:10pm to 5:00pm

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 Read more about Hongyuan Cao, Dept. Seminar

Lan Zhu, Dept. Seminar

Monday, November 7, 2016 - 4:10pm to 5:00pm

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 Read more about Lan Zhu, Dept. Seminar

Statistics Opening Seminar, Max Morris

Monday, August 29, 2016 - 4:10pm to 5:00pm

 (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. Read more about Statistics Opening Seminar, Max Morris

Guillermo Basulto-Elias, Computation of kernel deconvolution density estimators

Friday, August 5, 2016 - 11:00am to 11:45am

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. Read more about Guillermo Basulto-Elias, Computation of kernel deconvolution density estimators

Efficient Computing Workflow Group

Friday, February 5, 2016 - 2:00pm

Efficient Computing Workflow Group

 

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

Abstract:

We are excited to announce the formation of a new working group, the Efficient Computing Workflow group.  Read more about Efficient Computing Workflow Group

Design and Analysis of Definitive Screening Experiments (Zyskind Memorial Lecture)

Monday, October 19, 2015 - 4:15pm to Monday, December 21, 2015 - 2:30pm

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 Read more about Design and Analysis of Definitive Screening Experiments (Zyskind Memorial Lecture)

Relating Self-report and Accelerometer Physical Activity with Application to NHANES 2003-2004

Tuesday, September 15, 2015 - 3:15pm

Relating Self-report and Accelerometer Physical Activity with Application to NHANES 2003-2004

 

Date: Monday, September 15
Time: 3:10 pm -- 4:00 pm
Place: Carver 298
Speaker: Nicholas Beyler

Abstract:

Nicholas Beyler (presenting), Sarah Nusser, Wayne Fuller, Gregory Welk

Iowa State University

 

Abstract: Read more about Relating Self-report and Accelerometer Physical Activity with Application to NHANES 2003-2004

Local Structure Graph Models, Parameter Centering & Higher-Order Dependence Date: Thursday, July 31 Time: 2:00 pm -- 3:00 pm Place: 2113 Snedecor Hall Speaker: Emily Casleton Abstract: This talk describes a new class of models for random graphs or network

Friday, July 18, 2014 - 10:00am

Explorations of the lineup protocol for visual inference: application to high dimension, low sample size problems and metrics to assess the quality

  Read more about Local Structure Graph Models, Parameter Centering & Higher-Order Dependence Date: Thursday, July 31 Time: 2:00 pm -- 3:00 pm Place: 2113 Snedecor Hall Speaker: Emily Casleton Abstract: This talk describes a new class of models for random graphs or network

Total Survey Error

Friday, February 22, 2013 - 12:00pm

Total Survey Error

 

Date: Friday, February 22
Time: 12:00 pm -- 1:00 pm
Place: Snedecor 2113
Speaker: Yang Li, Bin Liu and Shu Yang, Department of Statistics, ISU

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.  

Read more about Total Survey Error>

Getting Started with Shiny

Wednesday, January 30, 2013 - 3:15pm

Getting Started with Shiny

 

Date: Wednesday, January 30
Time: 3:10 pm -- 4:00 pm
Place: Snedecor 2113
Speaker: Carson Sievert, Department of Statistics, ISU

Abstract:

Data-driven web applications are quickly gaining traction as a reliable vehicle for  Read more about Getting Started with Shiny

ASA Data Expo Challenge

Wednesday, January 23, 2013 - 3:15pm

ASA Data Expo Challenge

 

Date: Wednesday, January 23
Time: 3:10 am -- 4:00 pm
Place: 2113 Snedecor
Speaker: Heike Hofmann, Department of Statistics, Iowa State U

Abstract:

Welcome to Spring Semester Statistical Graphics Group Meeting

  Read more about ASA Data Expo Challenge

Resolving Isoform Expression using Digital Gene Expression Data

Monday, April 26, 2010 - 4:15pm

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 Read more about Resolving Isoform Expression using Digital Gene Expression Data