Канал: Chris Bilder

Time Series Analysis (2024), Week #15: ARCH models (part 2), final exam review

Time Series Analysis (2024), Week #15: ARCH models (part 2), final exam review

Time Series Analysis (2024), Week #14: Cross-correlation function, ARCH models (part 1)

Time Series Analysis (2024), Week #14: Cross-correlation function, ARCH models (part 1)

Time Series Analysis (2024), Week #13: Regression models with ARMA errors

Time Series Analysis (2024), Week #13: Regression models with ARMA errors

Time Series Analysis (2024), Week #12: SARIMA models (part2), ARFIMA models

Time Series Analysis (2024), Week #12: SARIMA models (part2), ARFIMA models

Time Series Analysis (2024), Week #11: Model building (part 2), SARIMA models

Time Series Analysis (2024), Week #11: Model building (part 2), SARIMA models

Time Series Analysis (2024), Week #10: Drift term, model building (part 1)

Time Series Analysis (2024), Week #10: Drift term, model building (part 1)

Time Series Analysis (2024), Week #9: Forecasting (part 2)

Time Series Analysis (2024), Week #9: Forecasting (part 2)

Time Series Analysis (2024), Week #8: Forecasting (part 1)

Time Series Analysis (2024), Week #8: Forecasting (part 1)

Time Series Analysis (2024), Week #7: ACF and PACF for estimated and true, ARIMA models, estimation

Time Series Analysis (2024), Week #7: ACF and PACF for estimated and true, ARIMA models, estimation

Time Series Analysis (2024), Week #5: Moving average models, ARMA models, ACF, PACF

Time Series Analysis (2024), Week #5: Moving average models, ARMA models, ACF, PACF

Time Series Analysis (2024), Week #4: Resolving non-stationarity, autoregressive models

Time Series Analysis (2024), Week #4: Resolving non-stationarity, autoregressive models

Time Series Analysis (2024), Week #3: Dependence, stationarity, estimate dependence

Time Series Analysis (2024), Week #3: Dependence, stationarity, estimate dependence

Time Series Analysis (2024), Week #2: Example data, basic models, dependence

Time Series Analysis (2024), Week #2: Example data, basic models, dependence

Time Series Analysis (2024), Week #1: introduction to class

Time Series Analysis (2024), Week #1: introduction to class

Applied Multivariate Statistical Analysis (2023) - Class #30, testing mean vectors

Applied Multivariate Statistical Analysis (2023) - Class #30, testing mean vectors

Applied Multivariate Statistical Analysis (2023) - Class #29, multinomial regression

Applied Multivariate Statistical Analysis (2023) - Class #29, multinomial regression

Applied Multivariate Statistical Analysis (2023) - Class #27, logistic and multinomial regression

Applied Multivariate Statistical Analysis (2023) - Class #27, logistic and multinomial regression

Applied Multivariate Statistical Analysis (2023) - Class #26, logistic regression, ROC curves

Applied Multivariate Statistical Analysis (2023) - Class #26, logistic regression, ROC curves

Applied Multivariate Statistical Analysis (2023) - Class #25, logistic regression

Applied Multivariate Statistical Analysis (2023) - Class #25, logistic regression

Applied Multivariate Statistical Analysis (2023) - Class #24, nearest neighbor analysis, logistic rg

Applied Multivariate Statistical Analysis (2023) - Class #24, nearest neighbor analysis, logistic rg

Applied Multivariate Statistical Analysis (2023) - Class #23, discrim. and nearest neighbor analysis

Applied Multivariate Statistical Analysis (2023) - Class #23, discrim. and nearest neighbor analysis

Applied Multivariate Statistical Analysis (2023) - Class #22, discriminant analysis

Applied Multivariate Statistical Analysis (2023) - Class #22, discriminant analysis

Applied Multivariate Statistical Analysis (2023) - Class #21, cluster and discriminant analysis

Applied Multivariate Statistical Analysis (2023) - Class #21, cluster and discriminant analysis

Applied Multivariate Statistical Analysis (2023) - Class #20, cluster analysis

Applied Multivariate Statistical Analysis (2023) - Class #20, cluster analysis

Applied Multivariate Statistical Analysis (2023) - Class #19, cluster analysis

Applied Multivariate Statistical Analysis (2023) - Class #19, cluster analysis

Applied Multivariate Statistical Analysis (2023) - Class #18, cluster analysis

Applied Multivariate Statistical Analysis (2023) - Class #18, cluster analysis

Applied Multivariate Statistical Analysis (2023) - Class #16, factor analysis, cluster analysis

Applied Multivariate Statistical Analysis (2023) - Class #16, factor analysis, cluster analysis

Applied Multivariate Statistical Analysis (2023) - Class #15, factor analysis

Applied Multivariate Statistical Analysis (2023) - Class #15, factor analysis

Applied Multivariate Statistical Analysis (2023) - Class #14, factor analysis

Applied Multivariate Statistical Analysis (2023) - Class #14, factor analysis

Applied Multivariate Statistical Analysis (2023) - Class #13, principal component & factor analysis

Applied Multivariate Statistical Analysis (2023) - Class #13, principal component & factor analysis

Applied Multivariate Statistical Analysis (2023) - Class #12, principal component analysis

Applied Multivariate Statistical Analysis (2023) - Class #12, principal component analysis

Applied Multivariate Statistical Analysis (2023) - Class #11, principal component analysis

Applied Multivariate Statistical Analysis (2023) - Class #11, principal component analysis

Applied Multivariate Statistical Analysis (2023) - Class #10, graphics & prin. component analysis

Applied Multivariate Statistical Analysis (2023) - Class #10, graphics & prin. component analysis

Applied Multivariate Statistical Analysis (2023) - Class #9, graphics

Applied Multivariate Statistical Analysis (2023) - Class #9, graphics

Applied Multivariate Statistical Analysis (2023) - Class #7, graphics

Applied Multivariate Statistical Analysis (2023) - Class #7, graphics

Applied Multivariate Statistical Analysis (2023) - Class #6, data-distributions-correlation, graphic

Applied Multivariate Statistical Analysis (2023) - Class #6, data-distributions-correlation, graphic

Applied Multivariate Statistical Analysis (2023) - Class #5, data-distributions-correlation

Applied Multivariate Statistical Analysis (2023) - Class #5, data-distributions-correlation

Applied Multivariate Statistical Analysis (2023) - Class #4, matrix algebra, data-distributions

Applied Multivariate Statistical Analysis (2023) - Class #4, matrix algebra, data-distributions

Applied Multivariate Statistical Analysis (2023) - Class #3, matrix algebra

Applied Multivariate Statistical Analysis (2023) - Class #3, matrix algebra

Applied Multivariate Statistical Analysis (2023) - Class #1

Applied Multivariate Statistical Analysis (2023) - Class #1

Categorical data analysis class on May 2, 2023

Categorical data analysis class on May 2, 2023

Cat video

Cat video

Model selection and evaluation: example

Model selection and evaluation: example

Model selection and evaluation: overdispersion, quasi-Poisson

Model selection and evaluation: overdispersion, quasi-Poisson

Model selection and evaluation: overdispersion, negative binomial

Model selection and evaluation: overdispersion, negative binomial

Model selection and evaluation: goodness-of-fit, influence

Model selection and evaluation: goodness-of-fit, influence

Model selection and evaluation: model assessment, residual plots

Model selection and evaluation: model assessment, residual plots

Model selection and evaluation: model assessment, residuals

Model selection and evaluation: model assessment, residuals

Count responses: Poisson rate regression

Count responses: Poisson rate regression

Model selection and evaluation: variable selection, stepwise search

Model selection and evaluation: variable selection, stepwise search

Model selection and evaluation: variable selection, AIC, all subsets

Model selection and evaluation: variable selection, AIC, all subsets

Count responses: Poisson regression for contingency tables, loglinear

Count responses: Poisson regression for contingency tables, loglinear

Count responses: Percentage change measure, PC

Count responses: Percentage change measure, PC

Count responses: Poisson regression, model and estimation

Count responses: Poisson regression, model and estimation

Count responses: Poisson distribution, inference

Count responses: Poisson distribution, inference

Exact inference: permutation test for independence

Exact inference: permutation test for independence

Exact inference: Fisher's exact test

Exact inference: Fisher's exact test

Multicategory responses: additional topics

Multicategory responses: additional topics

Multicategory responses: proportional odds regression, contingency table

Multicategory responses: proportional odds regression, contingency table

Multicategory responses: proportional odds regression, odds ratio

Multicategory responses: proportional odds regression, odds ratio

Multicategory responses: proportional odds regression, model & estimate

Multicategory responses: proportional odds regression, model & estimate

Multicategory responses: multinomial regression, contingency table

Multicategory responses: multinomial regression, contingency table

Multicategory responses: multinomial regression, odds ratio

Multicategory responses: multinomial regression, odds ratio

Multicategory responses: multinomial regression, model and estimation

Multicategory responses: multinomial regression, model and estimation

Multicategory responses: independence

Multicategory responses: independence

Multicategory responses: contingency table

Multicategory responses: contingency table

Multicategory responses: multinomial distribution

Multicategory responses: multinomial distribution

Logistic regression: generalized linear models

Logistic regression: generalized linear models

Logistic regression: convergence

Logistic regression: convergence

Logistic regression: categorical explanatory variables, odds ratios

Logistic regression: categorical explanatory variables, odds ratios

Logistic regression: categorical explanatory variables, form

Logistic regression: categorical explanatory variables, form

Logistic regression: transformations

Logistic regression: transformations

Logistic regression: interactions

Logistic regression: interactions

Logistic regression: probability of success, plots

Logistic regression: probability of success, plots

Logistic regression: probability of success, estimation and inference

Logistic regression: probability of success, estimation and inference

Logistic regression: odds ratios

Logistic regression: odds ratios

Logistic regression: hypothesis tests for regression parameters

Logistic regression: hypothesis tests for regression parameters

Logistic regression: regression parameter estimation - part 2

Logistic regression: regression parameter estimation - part 2

Logistic regression: regression parameter estimation - part 1

Logistic regression: regression parameter estimation - part 1

Logistic regression: introduction to model

Logistic regression: introduction to model

Two probability of success parameters: relative risk

Two probability of success parameters: relative risk

Two probability of success parameters: odds ratio

Two probability of success parameters: odds ratio

Two probability of success parameters: test pi1 - pi2

Two probability of success parameters: test pi1 - pi2

Two probability of success parameters: notation, model

Two probability of success parameters: notation, model

Two probability of success parameters: CI for pi1 - pi2

Two probability of success parameters: CI for pi1 - pi2

One probability of success parameter: hypothesis tests

One probability of success parameter: hypothesis tests

One probability of success parameter: MLE

One probability of success parameter: MLE

One probability of success parameter: confidence intervals

One probability of success parameter: confidence intervals

One probability of success parameter: true confidence level, coverage

One probability of success parameter: true confidence level, coverage

One probability of success parameter: binomial probability distribution

One probability of success parameter: binomial probability distribution

Time series analysis: Additional topics (More time series analysis)

Time series analysis: Additional topics (More time series analysis)

Time series analysis: Additional topics (GARCH models)

Time series analysis: Additional topics (GARCH models)

Time series analysis: Additional topics (ARCH models - ARIMA-ARCH example)

Time series analysis: Additional topics (ARCH models - ARIMA-ARCH example)

Time series analysis: Additional topics (ARCH models - simulated data example)

Time series analysis: Additional topics (ARCH models - simulated data example)

Time series analysis: Additional topics (ARCH models - introduction)

Time series analysis: Additional topics (ARCH models - introduction)

Time series analysis: Additional topics (regression models with ARMA errors)

Time series analysis: Additional topics (regression models with ARMA errors)

Time series analysis: Additional topics (multiple time series)

Time series analysis: Additional topics (multiple time series)

Time series analysis: Additional topics (ARFIMA models - additional examples)

Time series analysis: Additional topics (ARFIMA models - additional examples)

Time series analysis: Additional topics (ARFIMA models - introduction and simulated example)

Time series analysis: Additional topics (ARFIMA models - introduction and simulated example)

Time series analysis: ARIMA (seasonal ARIMA models - OSU example)

Time series analysis: ARIMA (seasonal ARIMA models - OSU example)

Time series analysis: ARIMA (seasonal ARIMA models - introduction and simulated example)

Time series analysis: ARIMA (seasonal ARIMA models - introduction and simulated example)

Time series analysis: ARIMA (model building - example)

Time series analysis: ARIMA (model building - example)

Time series analysis: ARIMA (model building - steps)

Time series analysis: ARIMA (model building - steps)

Time series analysis: ARIMA (forecasting - inference)

Time series analysis: ARIMA (forecasting - inference)

Time series analysis: ARIMA (forecasting - point estimation)

Time series analysis: ARIMA (forecasting - point estimation)

Time series analysis: ARIMA (drift term)

Time series analysis: ARIMA (drift term)

Alpha Seminar - Class #15 (guest speaker)

Alpha Seminar - Class #15 (guest speaker)

Time series analysis: ARIMA (estimation, maximum likelihood)

Time series analysis: ARIMA (estimation, maximum likelihood)

Time series analysis: ARIMA (estimation, method of moments)

Time series analysis: ARIMA (estimation, method of moments)

Time series analysis: ARIMA (Integrated part of an ARIMA model)

Time series analysis: ARIMA (Integrated part of an ARIMA model)

Time series analysis: ARIMA (Compare true to estimated ACFs and PACFs for ARMA models)

Time series analysis: ARIMA (Compare true to estimated ACFs and PACFs for ARMA models)

Time series analysis: ARIMA (partial autocorrelation function (PACF) for ARMA models)

Time series analysis: ARIMA (partial autocorrelation function (PACF) for ARMA models)

Time series analysis: ARIMA (autocorrelation function (ACF) for ARMA models)

Time series analysis: ARIMA (autocorrelation function (ACF) for ARMA models)

Time series analysis: ARIMA (autoregressive moving average models)

Time series analysis: ARIMA (autoregressive moving average models)

Time series analysis: ARIMA (moving average models)

Time series analysis: ARIMA (moving average models)

Time series analysis: ARIMA (autoregressive models)

Time series analysis: ARIMA (autoregressive models)

Time series analysis: Basics (resolving nonstationarity)

Time series analysis: Basics (resolving nonstationarity)

Time series analysis: Basics (estimation and inference)

Time series analysis: Basics (estimation and inference)

Time series analysis: Basics (stationarity)

Time series analysis: Basics (stationarity)

Time series analysis: Basics (dependence)

Time series analysis: Basics (dependence)

Time series analysis: Basics (basic models)

Time series analysis: Basics (basic models)

Time series analysis: Basics (example data)

Time series analysis: Basics (example data)

Introduction to R (part3, example with regression)

Introduction to R (part3, example with regression)

Bootstrap Methods and Their Application - Class #14

Bootstrap Methods and Their Application - Class #14

Bootstrap Methods and Their Application - Class #15

Bootstrap Methods and Their Application - Class #15

Bootstrap Methods and Their Application - Class #16

Bootstrap Methods and Their Application - Class #16

Bootstrap Methods and Their Application - Class #17

Bootstrap Methods and Their Application - Class #17

Bootstrap Methods and Their Application - Class #18

Bootstrap Methods and Their Application - Class #18

Bootstrap Methods and Their Application - Class #19

Bootstrap Methods and Their Application - Class #19

Bootstrap Methods and Their Application - Class #20

Bootstrap Methods and Their Application - Class #20

Bootstrap Methods and Their Application - Class #21

Bootstrap Methods and Their Application - Class #21

Bootstrap Methods and Their Application - Class #22

Bootstrap Methods and Their Application - Class #22

Bootstrap Methods and Their Application - Class #23

Bootstrap Methods and Their Application - Class #23

Bootstrap Methods and Their Application - Class #24

Bootstrap Methods and Their Application - Class #24

Bootstrap Methods and Their Application - Class #26

Bootstrap Methods and Their Application - Class #26

Bootstrap Methods and Their Application - Class #27

Bootstrap Methods and Their Application - Class #27

Bootstrap Methods and Their Application - Class #29

Bootstrap Methods and Their Application - Class #29

Bootstrap Methods and Their Application - Class #30

Bootstrap Methods and Their Application - Class #30

Bootstrap Methods and Their Application - Class #28

Bootstrap Methods and Their Application - Class #28

Joint Statistical Meetings 2021 - Alpha Seminar: A Course for New Graduate Students in Statistics

Joint Statistical Meetings 2021 - Alpha Seminar: A Course for New Graduate Students in Statistics

HIV vaccine clinical trial results

HIV vaccine clinical trial results

Introduction to Mathematical Statistics: Likelihood (likelihood ratio tests)

Introduction to Mathematical Statistics: Likelihood (likelihood ratio tests)

Introduction to Mathematical Statistics: Likelihood (working with MLEs)

Introduction to Mathematical Statistics: Likelihood (working with MLEs)

Introduction to Mathematical Statistics: Likelihood (maximum likelihood estimation)

Introduction to Mathematical Statistics: Likelihood (maximum likelihood estimation)

Introduction to Mathematical Statistics: More inference (difference of two proportions)

Introduction to Mathematical Statistics: More inference (difference of two proportions)

Introduction to Mathematical Statistics: More inference (proportion)

Introduction to Mathematical Statistics: More inference (proportion)

Introduction to Mathematical Statistics: More inference (difference two means, dependent samples)

Introduction to Mathematical Statistics: More inference (difference two means, dependent samples)

Introduction to Mathematical Statistics: More inference (difference two means, independent samples)

Introduction to Mathematical Statistics: More inference (difference two means, independent samples)

Introduction to Mathematical Statistics: Hypothesis tests (one-sided)

Introduction to Mathematical Statistics: Hypothesis tests (one-sided)

Introduction to Mathematical Statistics: Hypothesis tests (two-sided)

Introduction to Mathematical Statistics: Hypothesis tests (two-sided)

Introduction to Mathematical Statistics: Confidence intervals (more)

Introduction to Mathematical Statistics: Confidence intervals (more)

Introduction to Mathematical Statistics: Confidence intervals (mean)

Introduction to Mathematical Statistics: Confidence intervals (mean)

Introduction to Mathematical Statistics: Sampling distributions (chi-square)

Introduction to Mathematical Statistics: Sampling distributions (chi-square)

Introduction to Mathematical Statistics: Sampling distributions (central limit theorem)

Introduction to Mathematical Statistics: Sampling distributions (central limit theorem)

Introduction to Mathematical Statistics: Sampling distributions (sample)

Introduction to Mathematical Statistics: Sampling distributions (sample)

Introduction to Mathematical Statistics: Specific probability distributions (gamma)

Introduction to Mathematical Statistics: Specific probability distributions (gamma)

Introduction to Mathematical Statistics: Specific probability distributions (normal)

Introduction to Mathematical Statistics: Specific probability distributions (normal)

Introduction to Mathematical Statistics: Specific probability distributions (continuous uniform)

Introduction to Mathematical Statistics: Specific probability distributions (continuous uniform)

Introduction to Mathematical Statistics: Specific probability distributions (Poisson)

Introduction to Mathematical Statistics: Specific probability distributions (Poisson)

Introduction to Mathematical Statistics: Specific probability distributions (binomial)

Introduction to Mathematical Statistics: Specific probability distributions (binomial)

Introduction to Mathematical Statistics: Specific probability distributions (discrete uniform)

Introduction to Mathematical Statistics: Specific probability distributions (discrete uniform)

Introduction to Mathematical Statistics: Expectation (Chebyshev's rule)

Introduction to Mathematical Statistics: Expectation (Chebyshev's rule)

Introduction to Mathematical Statistics: Expectation (linear combinations)

Introduction to Mathematical Statistics: Expectation (linear combinations)

Introduction to Mathematical Statistics: Expectation (covariance)

Introduction to Mathematical Statistics: Expectation (covariance)

Introduction to Mathematical Statistics: Expectation (variance)

Introduction to Mathematical Statistics: Expectation (variance)

Introduction to Mathematical Statistics: Expectation (expected value)

Introduction to Mathematical Statistics: Expectation (expected value)

Introduction to Mathematical Statistics: Probability (joint probability distribution functions)

Introduction to Mathematical Statistics: Probability (joint probability distribution functions)

Introduction to Mathematical Statistics: Probability (integration)

Introduction to Mathematical Statistics: Probability (integration)

Introduction to Mathematical Statistics: Probability (continuous random variables)

Introduction to Mathematical Statistics: Probability (continuous random variables)

Introduction to Mathematical Statistics: Probability (discrete random variables)

Introduction to Mathematical Statistics: Probability (discrete random variables)

Introduction to Mathematical Statistics: Probability (introduction)

Introduction to Mathematical Statistics: Probability (introduction)

Introduction to Mathematical Statistics: Summarize data (numerical)

Introduction to Mathematical Statistics: Summarize data (numerical)

Introduction to Mathematical Statistics: Summarize data (graphical)

Introduction to Mathematical Statistics: Summarize data (graphical)

Introduction to Mathematical Statistics: Summarize data (numerical)

Introduction to Mathematical Statistics: Summarize data (numerical)

Introduction to Mathematical Statistics: Section 1 (introduction)

Introduction to Mathematical Statistics: Section 1 (introduction)

Introduction to R (part3, example)

Introduction to R (part3, example)

Introduction to R (part 2, program editors)

Introduction to R (part 2, program editors)

Statistical Methods in Research: Regression (multiple linear regression)

Statistical Methods in Research: Regression (multiple linear regression)

Statistical Methods in Research: Regression (coefficients of determination and correlation)

Statistical Methods in Research: Regression (coefficients of determination and correlation)

Statistical Methods in Research: Regression (inference)

Statistical Methods in Research: Regression (inference)

Statistical Methods in Research: Probability (discrete random variables)

Statistical Methods in Research: Probability (discrete random variables)

Statistical Methods in Research: Regression (variability)

Statistical Methods in Research: Regression (variability)

Introduction to R (part4, object-oriented language)

Introduction to R (part4, object-oriented language)

Statistical Methods in Research: Regression (least squares)

Statistical Methods in Research: Regression (least squares)

Statistical Methods in Research: Regression (introduction)

Statistical Methods in Research: Regression (introduction)

Statistical Methods in Research: ANOVA (randomized complete block design)

Statistical Methods in Research: ANOVA (randomized complete block design)

Statistical Methods in Research: ANOVA (multiple comparisons)

Statistical Methods in Research: ANOVA (multiple comparisons)

Statistical Methods in Research: ANOVA (completely randomized design, CRD)

Statistical Methods in Research: ANOVA (completely randomized design, CRD)

Statistical Methods in Research: ANOVA (logic behind it)

Statistical Methods in Research: ANOVA (logic behind it)

Statistical Methods in Research: ANOVA (introduction)

Statistical Methods in Research: ANOVA (introduction)

Statistical Methods in Research: Inference for variances (two variances)

Statistical Methods in Research: Inference for variances (two variances)

Statistical Methods in Research: Inference for variances (one variance)

Statistical Methods in Research: Inference for variances (one variance)

Statistical Methods in Research: Inference for proportions (two proportions)

Statistical Methods in Research: Inference for proportions (two proportions)

Statistical Methods in Research: Inference for proportions (sample size)

Statistical Methods in Research: Inference for proportions (sample size)

Statistical Methods in Research: Inference for proportions (one proportion)

Statistical Methods in Research: Inference for proportions (one proportion)

Statistical Methods in Research: Inference for two means (dependent samples)

Statistical Methods in Research: Inference for two means (dependent samples)

Statistical Methods in Research: Inference for two means (independent samples)

Statistical Methods in Research: Inference for two means (independent samples)

Statistical Methods in Research: Inference for one mean (hypothesis tests, power)

Statistical Methods in Research: Inference for one mean (hypothesis tests, power)

Statistical Methods in Research: Inference for one mean (hypothesis tests, one tail)

Statistical Methods in Research: Inference for one mean (hypothesis tests, one tail)

Statistical Methods in Research: Inference for one mean (hypothesis tests, two tails)

Statistical Methods in Research: Inference for one mean (hypothesis tests, two tails)