Executive Development Programme in Statistical Inference: Likelihood Techniques

This Executive Development Programme in Statistical Inference: Likelihood Techniques is designed for business leaders and data-driven managers seeking to enhance their decision-making through advanced statistical methods. Participants will gain expertise in likelihood-based inference, essential for analyzing complex data sets and improving strategic outcomes in their organizations.

Upon completion, attendees will be able to apply likelihood techniques to real-world business problems, evaluate the reliability of statistical models, and communicate insights effectively to non-technical stakeholders, thereby driving informed decisions and innovation.

1. 1. Introduction to Statistical Inference: Learners will study the fundamental principles of statistical inference, including estimation and hypothesis testing. They will gain foundational skills in understanding data variability and making inferences about population parameters based on sample data.
2. 2. Likelihood Principles and Maximum Likelihood Estimation: This module covers the principles of likelihood and how to use maximum likelihood estimation to infer parameters from data. Learners will develop skills in formulating likelihood functions and estimating parameters with maximum likelihood.
3. 3. Likelihood Ratio Tests: Learners will explore likelihood ratio tests for comparing nested models and will gain practical skills in conducting these tests to evaluate hypotheses about statistical models.
4. 4. Confidence Intervals and Likelihood: This module focuses on constructing confidence intervals using likelihood methods. Learners will learn to interpret likelihood-based confidence intervals and understand their role in statistical inference.
5. 5. Bayesian Inference and Likelihood: An introduction to Bayesian inference, learners will study how likelihood combines with prior information to form posterior distributions. They will gain skills in applying Bayesian techniques to infer parameters and make predictions.
6. 6. Advanced Likelihood Methods for Missing Data: This module covers advanced methods for handling missing data using likelihood techniques. Learners will learn to implement likelihood-based imputation and estimation methods.
7. 7. Likelihood for Time Series Analysis: Learners will study how to apply likelihood methods to time series data, focusing on models such as ARIMA and GARCH. They will gain skills in estimating parameters and conducting inference for time-dependent data.
8. 8. Likelihood in Machine Learning: This module explores the use of likelihood in machine learning algorithms, particularly in classification and regression models. Learners will develop skills in applying likelihood-based methods to predictive modeling.
9. 9. Simulation and Likelihood-Based Inference: Learners will study the use of simulation techniques in likelihood-based inference, including bootstrapping and Monte Carlo methods. They will gain practical skills in simulating data and using simulations to validate inference methods.
10. 10. Advanced Topics in Likelihood Techniques: An advanced overview of current research topics in likelihood techniques, including composite likelihood, profile likelihood, and likelihood ratio tests in high-dimensional settings. Learners will explore cutting-edge methods and their applications.