Seminars

Bootstrap inference for the finite population total under complex sampling designs

Tuesday, January 16, 2018 - 3:30pm to 4:30pm
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Speaker: Zhonglei Wang / Abstract: Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results in survey sampling if the sampling design is ignored. Most studies about bootstrap-based inference are developed under simple random sampling and stratified random sampling. In this work, we propose a new bootstrap method applicable to some complex sampling designs, including Poisson sampling and probability proportional to size sampling. Read more about Bootstrap inference for the finite population total under complex sampling designs

Data Integration for Big Data Analysis

Tuesday, January 23, 2018 - 3:30pm to 4:30pm
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Speaker: Jae-kwang Kim / Abstract: In analyzing big data for finite population inference, it is critical to adjust for the selection bias in the big data. Using an independent probability sampling, the selection bias of big data can be removed safely. Such technique can be called data integration for big data analysis. In this talk, several methods of data integration are presented in the context of big data analysis. Results from simulation studies are also presented. Read more about Data Integration for Big Data Analysis

Inverse Conditional Probability Weighting with Clustered Data in Causal Inference

Tuesday, January 30, 2018 - 3:30pm to 4:30pm
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Speaker: Zhulin He / Abstract: Estimating the average treatment causal effect in clustered data often involves dealing with unmeasured cluster-specific confounding variable. The unmeasured cluster-specific confounding variable may be correlated with the measured covariates and the outcome. When such correlations are ignored, the causal effect estimation can be biased. Read more about Inverse Conditional Probability Weighting with Clustered Data in Causal Inference

Semiparametric Estimation and Testing for Nonignorable Nonresponse

Tuesday, February 6, 2018 - 3:30pm to 4:30pm
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Speaker: Hejian Sang / Abstract: Statistical inference with nonresponse is quite challenging, especially when the response mechanism is not missing at random. The existing methods often require correct model specifications for both the outcome regression model and the response model. However, due to nonresponse, both model assumptions cannot be verified from the data and model misspecification can seriously lead to biased inferences. To overcome this limitation, we develop a robust semiparametric method based on the profile likelihood. Read more about Semiparametric Estimation and Testing for Nonignorable Nonresponse

Asynchronous stochastic gradient descent with unbounded delay on nonconvex problem

Tuesday, February 13, 2018 - 3:30pm to 4:30pm
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Speaker: Xin Zhang / Abstract:  Parallel distributed implementations of stochastic gradient, especially asynchronous scheme, have been widely applied in optimizing nonconvex problems. So far, several works have been done to analyze the asymptotic convergence rate of asynchronous gradient descent method. But these works have been restricted by bounded delay. In our work, we focus on the asynchronous stochastic gradient descent methods with unbounded delay on nonconvex optimization problem. Read more about Asynchronous stochastic gradient descent with unbounded delay on nonconvex problem

Mass Imputation for Two-phase Sampling

Tuesday, February 20, 2018 - 3:30pm to 4:30pm
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Speaker: Seho Park / Abstract: Two-phase sampling is a convenient and economical sampling design that sample selection is conducted in two phases. A large sample is collected from population measuring auxiliary variables and then a smaller sample is collected measuring study variables by incorporating the auxiliary information. The structure of two-phase sample can be seen as a missing data problem: some are observed and the others are missing. Read more about Mass Imputation for Two-phase Sampling

Cluster-level inference under element sampling

Tuesday, February 27, 2018 - 3:30pm to 4:30pm
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Speaker: Danhyang Lee / Abstract: A two-level model is a useful tool for analyzing clustered data. However, when we collect data using element sampling, the classical estimation method for a two-level model can be biased. We propose a general estimation method for two-level models by incorporating some induced cluster-level weights due to sampling design, when weight information is only available at the element level. It also uses an EM algorithm based on the approximate predictive distribution of the cluster-specific random effects. Read more about Cluster-level inference under element sampling

A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach

Tuesday, March 6, 2018 - 3:30pm to 4:30pm
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Speaker: Xin Wang / Abstract:  The generalized linear model (GLM) is a class of regression models where the means of the response variables and the linear predictors are joined through a link function. Standard GLM assumes the link function is fixed, and one can form more flexible GLM by either estimating the flexible link function from a parametric family of link functions or estimating it nonparametically. Read more about A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach

Sample Design and Statistical Estimation for the 2016 Bureau of Land Management Rangeland Survey

Tuesday, March 20, 2018 - 3:30pm to 4:30pm
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Speaker: Hengfang Wang / Abstract: The Department of Interior Bureau of Land Management (BLM) is interested in obtaining statistically valid estimates of rangeland conditions on the land. Previous Bureau of Land Management Rangeland Surveys (BLM Rangeland Surveys) relied on NRI sampling units for sample frame construction. Because we are short of NRI sampling units in some states with large BLM acres, such as Nevada, a new approach was developed in 2014 to draw independent samples for BLM Rangeland Surveys. Read more about Sample Design and Statistical Estimation for the 2016 Bureau of Land Management Rangeland Survey

Variance Estimation for Imputed Data

Tuesday, March 27, 2018 - 3:30pm to 4:30pm
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Speaker: Xiaofei Zhang / Abstract: Imputation is a procedure used to complete records for a subsample of a large sample, where large sample has complete records. We consider nearest neighbor imputation (NNI) and give some theoretical properties for the NNI estimator. An example is the National Resources Inventory (NRI), a longitudinal survey in which some units are observed every year and some units are observed periodically. Data for non observations are imputed using the units observed every. Read more about Variance Estimation for Imputed Data

2018 Metro Market Survey

Tuesday, April 3, 2018 - 3:30pm to 4:30pm
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Speaker: Zhenzhong Wang / Abstract: CSSM has completed 2017 Pet Demographic Survey (2017) which is conducted every five years. Metro Market Demand Survey (MMDS) is a smaller survey conducted every year to improve sub-regional accuracy of PDS estimates and update the nationwide estimates. In each year it contains six surveys for six different metro areas. Read more about 2018 Metro Market Survey

Gap-filling for large spatio-temporal satellite images

Tuesday, April 10, 2018 - 3:30pm to 4:30pm
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Speaker: Weicheng Zhu / Abstract:  Many scientific applications and signal processing algorithms require complete satellite data, while often times the quality of the remote sensed satellite data is not good due to cloud cover. We introduce a gap-filling algorithm for spatio-temporal satellite images and show its applications on real data sets Landsat data and Moderate Resolution Imaging Spectroradiometer(MODIS) data.  Read more about Gap-filling for large spatio-temporal satellite images

Quantile regression analysis of survey data under informative sampling

Tuesday, April 17, 2018 - 3:30pm to 4:30pm
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Speaker: Sixia Chen, University of Oklahoma Health Science Center / Abstract:  For complex survey data, the parameter estimates in a quantile regression analysis can be obtained by minimizing a weighted objective function with weights being the original design weights.  However, when the complex survey sampling design is informative, i.e., when the design weights are correlated with the study variable even after conditioning on other covariates, the efficiency of aforementioned design-weighted estimator may be further Read more about Quantile regression analysis of survey data under informative sampling

NRI Road segmentation using U‑Net

Tuesday, April 24, 2018 - 3:30pm to 4:00pm
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Speaker: Lei Zhou / Abstract: Road detection using remote sensing images is a useful method and has many applications in the real world settings. Recently, deep learning models have shown superior results in image segmentation comparing to previous methods. National Resources Inventory (NRI) has remote sensing landcover images and human labeled road layers. Our work is to apply deep learning model U-Net on NRI remote sensing image data to detect road. Read more about NRI Road segmentation using U‑Net

Monitoring Surface Area Change in Iowa's Water Bodies

Tuesday, April 24, 2018 - 4:00pm to 4:30pm
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Speaker: Charlie  Labuzzetta / Abstract: The National Resource Inventory (NRI) estimates trends in environmental resources on non-Federal lands throughout the United States. Annual estimates of the surface area of large water bodies (LWB) throughout the nation are reported as part of the NRI database. Read more about Monitoring Surface Area Change in Iowa's Water Bodies