Nonparametric Regression with Correlated Errors Jean Opsomer Iowa State University Yuedong Wang University of California, Santa Barbara

Source URL: www.pstat.ucsb.edu

• Statistics
• Regression analysis
• Autocorrelation
• Linear regression
• Polynomial regression
• Nonparametric regression
• Kernel regression
• Correlation and dependence
• Errors and residuals
• Degrees of freedom
• Variance
• Normal distribution

IEEE TRANSACTION ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1 Human Pose Estimation with Regression by Fusing Multi-View Visual Information

Source URL: www.ifp.illinois.edu

• Artificial intelligence
• Statistics
• Vision
• Computer vision
• Robot control
• Regression analysis
• Estimation theory
• Pose
• 3D pose estimation
• Pattern recognition
• Linear regression
• Nonparametric regression

The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series∗ Heejoon Han† Oliver Linton‡

Source URL: www.cb.cityu.edu.hk

• Statistics
• Regression analysis
• Time series analysis
• Statistical inference
• Quantile
• Bootstrapping
• Autocorrelation
• Autoregressive conditional heteroskedasticity

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

Source URL: remotesensing.montana.edu

• Statistics
• Prediction
• Time
• Time series analysis
• Remote sensing
• Biogeography
• Normalized Difference Vegetation Index
• Phenology
• Enhanced vegetation index
• Forecasting
• Landsat 7
• Regression analysis

INSTRUMENTAL VARIABLES ESTIMATION WITH MANY WEAK INSTRUMENTS USING REGULARIZED JIVE CHRISTIAN HANSEN AND DAMIAN KOZBUR Abstract. We consider instrumental variables regression in models where the number of available instr

Source URL: www.n.ethz.ch

• Statistics
• Estimation theory
• Statistical theory
• Regression analysis
• Statistical inference
• Statistical methods
• Linear regression
• Instrumental variable
• Estimator
• Asymptotic theory
• Shrinkage estimator
• M-estimator

We connect high-dimensional subset selection and submodular maximization. Our results extend the work of Das and Kempefrom the setting of linear regression to arbitrary objective functions. This connection allows

Source URL: mmds-data.org

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection Hassan Ghasemzadeh Mohammadi∗ , Pierre-Emmanuel Gaillardon∗ , Majid Yazdani† , Giovanni De Micheli∗ Integra

Source URL: majid.yazdani.me

• Software
• Estimation theory
• Statistics
• Machine learning
• Application software
• Actuarial science
• Regression analysis
• SPICE
• Pattern recognition
• Linear regression
• Structural estimation
• Semiconductor device modeling

Department of Public Health University of Copenhagen Advanced Epidemiology and Biostatistics Fall 2016

Source URL: mph.ku.dk

• Demography
• Statistics
• Public health
• Regression analysis
• Estimation theory
• Academia
• Bioinformatics
• Biostatistics
• Epidemiology
• Euler
• Linear regression
• Logistic regression

CSIRO PUBLISHING www.publish.csiro.au/journals/ajsr Australian Journal of Soil Research, 2003, 41, 1403–1422

Source URL: www.css.cornell.edu

• Statistics
• Regression analysis
• Estimation theory
• Linear regression
• Mean squared error
• Errors and residuals
• Autocorrelation
• Prediction
• Sampling
• Multicollinearity
• Regression-Kriging

Understanding spatial nonstationarity in the influence of topography and vegetation on patterns of snow depth and snow water equivalent (SWE) can improve distributed SWE models, improve river runoff forecasts, and guide

Source URL: mmds-data.org

• Statistics
• Signal processing
• Estimation theory
• Meteorology
• Precipitation
• Snow
• Water ice
• Regression analysis
• Lidar
• Autoregressive model
• Water