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  1. Sp12-OIT-267-01 : Data and Decisions - Accelerated. 2012 Spring

    Bayati, Mohsen
    Stanford (Calif.), 2012

    Data and Decisions - Accelerated is a first-year MBA course in probability, statistics, multiple regression analysis, and decision trees for students with strong quantitative backgrounds. Probability provides the foundation for modeling uncertainties. Statistics provides techniques for interpreting data, permitting managers to use small amounts of information to answer larger questions. Regression analysis provides a method for determining the relationship between a dependent variable and predictor variables. Decision tree analysis consists of quantitative approaches to decision making under uncertainty. Students taking this course need to be comfortable with mathematical notation, algebra, and some calculus. If you are not confident with your quantitative abilities, then you should enroll in OIT 265. Accelerated DandD will cover material covered in OIT 265 faster and in more depth. One main difference is that Accelerated DandD will cover the additional topics of advanced multiple regression analysis (e.g., correction for autocorrelation), discrete dependent variable models, and panel data. A multiple regression group project is required.Data and Decisions - Accelerated is a first-year MBA course in probability, statistics, multiple regression analysis, and decision trees for students with strong quantitative backgrounds. Probability provides the foundation for modeling uncertainties. Statistics provides techniques for interpreting data, permitting managers to use small amounts of information to answer larger questions. Regression analysis provides a method for determining the relationship between a dependent variable and predictor variables. Decision tree analysis consists of quantitative approaches to decision making under uncertainty. Students taking this course need to be comfortable with mathematical notation, algebra, and some calculus. If you are not confident with your quantitative abilities, then you should enroll in OIT 265. Accelerated DandD will cover material covered in OIT 265 faster and in more depth. One main difference is that Accelerated DandD will cover the additional topics of advanced multiple regression analysis (e.g., correction for autocorrelation), discrete dependent variable models, and panel data. A multiple regression group project is required.

  2. A nonparametric measure of conditional dependence

    Azadkia, Mona
    [Stanford, California] : [Stanford University], 2020

    There are numerous problems where one needs to quantify the dependence between two random variables and how this dependence changes by conditioning on a third random variable. Correlated random variables might become independent when we observe a third random variable or two independent random variables might become dependent after conditioning on the third one. Thanks to the wide potential application range e.g., bioinformatics, economics, psychology, etc, finding efficient measures of conditional dependence has been an active area of research in many subareas of statistics and machine learning. However, the literature on measures of conditional dependence is not so large, especially in the non-parametric setting. We introduce two novel measures of conditional dependence, and propose estimators based on i.i.d. samples. Using these statistics, we devise a new variable selection algorithm, called Feature Ordering by Conditional Independence (FOCI). FOCI is model-free with no tuning parameters and is provably consistent under sparsity assumptions. We provide a number of example application analyses to both synthetic and real datasets

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