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seminars:mas_capstone

Capstone Seminar

Fall 2017

  • September 13
    Time: 1:10–1:25 pm
    Speaker: Wangshu Tu
    Title: Parameters Selection and Comparison in Guassian Kernel SVM

    Abstract: In SVM(Support Vector Machines), it is not so clear that which kernel to choose and how to select proper parameter in kernel function. One of them: Gaussian radial basis function(RBF) is very popular because of only single parameter needs to be determined. In this short talk, it will present different results of applying RBF kernel in binary classification case–Gender Recognition by Voice, with different pairs of (C, r), where C is a regularization parameter to constrain the range of Lagrangian coefficients in dual function F_D, r is reciprocal of single parameter sigma^2 in RBF kernel. For each pair (C, r), compute 10-folder Cross Validation(CV/10) misclassification rate, and it indicates the rate will decrease when C or r increases. The smallest CV/10 misclassification rate among all pairs of (C, r) is also better than LDA and classification tree.

  • September 13
    Time: 1:25–1:40 pm
    Speaker: Xiang Wang
    Title: Use LDA and QDA To Discriminate Diabetes Data

    Abstract: Diabetes data resulted from a study conducted at the Stanford Clinical Research Center of the relationship between the three clinical classifications and five measurements for 145 instances. It helps the diagnosis and appropriate treatment to the diabetes patients.

    We draw a scatterplot matrix of all five variables representing the problematic multivari- ate Gaussian distributions, and the assumption of equal covariance matrices is inappropriate, which play the negative roles in LDA(Linear Discriminant Analysis) and QDA(Quadratic Discriminant Analysis). We use LDA and QDA to discriminate the clinical classifications by the five variables, then draw the 2D-scatter-plot of the first two discriminating functions to show that LDA is subject to outliers, but QDA relatively improves the classification by nonlinear discrimination.

    The misclassification rates in the leave-one-out cross-validation are 11% for LDA and 9.7% for QDA. The fitting for 145 instances indicates LDA and QDA can be quite flexible.

  • September 13
    Time: 1:40–1:55 pm
    Speaker: Joshua Rovou
    Title: Managing Multinomial Data: Using Aids and Examples from Dr. Ganggang Xu and Julian Faraway's “Generalized Linear Models”

    Abstract: Multinomial data requires careful thought to properly analyze. There are various forms that multinomial data can take that can easily be misclassified, leading to false conclusions. Understanding when, why, and how to recognize and apply these forms and their assumptions allows a data scientist to be mindful of best practices when fitting multinomial models. This paper provides an overview, suggestions, and examples of classifying multinomial data. In addition, this paper provides discussions on applying the appropriate assumptions and models in the R programming language, using the problem and data sets in Faraway’s “Generalized Linear Models” Chapter 5 as case studies. These examples seek to illustrate best practices when dealing with multinomial data for the student data scientist.

  • September 13
    Time: 1:55–2:10 pm
    Speaker: Hao Wang
    Title: Forest Cover Type prediction

    Abstract: This report is based on the data from UCI machine learning website and the raw data was determined from the US Forest Service (USFS) Region 2 Resource Information System (RIS). There are totally 581,012 observations in this data set and each observation is a 30m x 30m cell containing 54 attributes, which consist of 10 quantitative variables, 4 binary wilderness areas and 40 binary soil type variables. The forest cover type is basically a classification problem. By looking from the previous work from Blackard's investigation on this topic (1), we think that we could try using several classifiers such as SVM,K-NN, decision tree and gradient boost model in modeling to increase the accuracy in prediction.

  • September 20
    Time: 1:10–1:25 pm
    Speaker: Xiaolin Tang
    Title: MODEL SELECTION

    Abstract: TBA.For a dataset, we can come up with plenty of models, however, there should be a best one through all of them. Therefore, we need some criteria to evaluate the model, and a method to find out it, here comes model selection.

    My presentation will be presented with two parts: first, I will give a quickly review of some basic concepts, including the criteria of a good model and the method to do model selection. Second, I will display several questions of chapter 10 in “Faraway I”(Linear Models with R, second Edition) and talk about the solutions. For Question 10.5, it's about how outliers influence the result of model selection, we use the data with and without the outliers to do model selection separately, and our conclusion is we can obtain the same model, however, the estimated coefficient will be very different for these two models.

  • September 20
    Time: 1:25–1:40 pm
    Speaker: Shaofei Zhao
    Title: Use Dataset aatemp to Predict Temperature in the Future

    Abstract: Dataset aatemp is from the U.S. Historical Climatology Network. They are the annual mean temperatures in Ann Arbor, Michigan going back about 150 years. The data contains 115 observations on two variables, year is the year from 1854 to 2000, temp is the annual mean temperature. Our motivation is that by analyzing the data, we may give a reasonable prediction of the mean temperature in 2020. To analyze, we need firstly check the assumptions of error terms, such as constant variance, normality and outliers. Then we proceed linear regression considering response transformation and predictor transformation, in the long talk we may also consider some methods of time series to achieve a better model. At the moment, we use a model with 3-degree polynomials, which can increase our R^2 from 0.05 to 0.13, and base on this model, we may predict the temperature of 2020 is about 46 degrees.

  • September 20
    Time: 1:40–1:55 pm
    Speaker: McInroy,Alexander
    Title: Binomial Regression and its Applications in Medical Diagnosis

    Abstract: Regression analysis remains one of the most directly applicable tools in the field of statistics and machine learning today. In particular, binomial regression is especially useful for diagnosing medical conditions where a test leads to a positive or negative result. This talk will use examples from Extending the Linear Model with R, Faraway to illustrate this. Technical topics covered will be data analysis, data interpretation, model selection, and model prediction. Furthermore, selecting appropriate cutoff points for a binomial model’s response will be discussed, since mitigating type I and type II error is a subjective balancing act depending on the situation..

  • September 20
    Time: 1:55–2:10 pm
    Speaker: Schepis,Michael
    Title: Classification Methodology

    Abstract: One of the primary motivations of analyzing data is our ability to accurately categorize it. This is the purpose of classification; Different statistical techniques allow us to divide categorical data into relevant groups, however this ability is only as useful as our ability to interpret these groups in a meaningful way. Most data of interest cannot be perfectly separated without error, so it is also crucial to analyze mis-classification rates with each of these statistical tools. In this talk, we will provide relevant examples from Alan J. Izenman's Multivariate Statistical Techniques and discuss the tools of Linear Discriminant analysis, Bayes Rule, and Quadratic Discriminant Analysis and when each of those techniques would be most appropriate and how to create a confusion matrix to examine the accuracy of our findings..

  • September 27
    Time: 1:10–1:25 pm
    Speaker: Yanwei Jiang
    Title: TBA

    Abstract: TBA.

  • September 27
    Time: 1:25–1:40 pm
    Speaker: Yifei Zeng
    Title: TBA

    Abstract: TBA.

  • September 27
    Time: 1:40–1:55 pm
    Speaker: Gang Cheng
    Title: TBA

    Abstract: In ordinary least square regression, we assume the error term $\epsilon$ is independent and identically distributed. Furthermore, in order to carry out the usual statistical inference, we also assume the error term are normally distributed. However, in many cases, this assumption always violated and we have to consider alternatives. (i) When the errors are dependent, like time series, we use \emph{Generalized Least Squares}(GLS); (ii) When the errors are independent, but not identically distributed, we can use \emph{Weighted Least Squares}. (iii)When the errors are not normally distributed, we can use \emph{Robust Regression}. In this talk, I will mainly focus on the theory part of these regressions and one or two example(s) of these.

  • September 27
    Time: 1:55–2:10 pm
    Speaker: Chenxi Wang
    Title: TBA

    Abstract: My topic for seminar is focused on Chapter 14 of the book Linear Models of R by Faraway. This chapter mainly talks about categorical predictors for linear regression models. I will give brief talk about basic concepts related to categorical predictors. Also, together with exercises at the end of this chapter, I will give specific examples on how to deal with categorical factors in regression analysis in practical world.

  • October 4
    Time: 1:10–1:40 pm
    Speaker: Hao Wang
    Title: TBA

    Abstract: TBA.

  • October 4
    Time: 1:40–2:10 pm
    Speaker: Chenxi Wang
    Title: TBA

    Abstract: TBA.

  • October 11
    Time: 1:10–1:40 pm
    Speaker: Shaofei Zhao
    Title: TBA

    Abstract: TBA.

  • October 11
    Time: 1:40–2:10 pm
    Speaker: Yifei Zeng
    Title: TBA

    Abstract: TBA.

  • October 18
    Time: 1:10–1:40 pm
    Speaker: Gang Cheng
    Title: TBA

    Abstract: TBA.

  • October 18
    Time: 1:40–2:10 pm
    Speaker: Schepis,Michael
    Title: TBA

    Abstract: TBA.

  • October 25
    Time: 1:10–1:40 pm
    Speaker: McInroy,Alexander
    Title: TBA

    Abstract: TBA.

  • October 25
    Time: 1:40–2:10 pm
    Speaker: Rovou,Joshua
    Title: TBA

    Abstract: TBA.

  • November 1
    Time: 1:10–1:40 pm
    Speaker: Xiaolin Tang
    Title: TBA

    Abstract: TBA.

  • November 1
    Time: 1:40–2:10 pm
    Speaker: Xiang Wang
    Title: TBA

    Abstract: TBA.

  • November 8
    Time: 1:10–1:40 pm
    Speaker: Wangshu Tu
    Title: TBA

    Abstract: TBA.

  • November 8
    Time: 1:40–2:10 pm
    Speaker: Yangwei Jiang
    Title: TBA

    Abstract: TBA.

Spring 2017

  • February 23
    Time: 1:15–2:15 pm
    Speaker: Yu Hu
    Title: Vehicle's Fuel Economy Data Analysis

    Abstract: This project I did aimed at providing reliable estimates for comparing vehicles in 2016. The purpose is to help car buyers choose the most fuel-efficient vehicle that meets their needs. Using the linear regression knowledge to analysis the correlation between each factor(vehicles's weight, cylinder, engine size, etc.) and response(fuel economy).

  • March 2
    Time: 1:15–2:15 pm
    Speaker: Hao Wang
    Title: On testing independence and goodness of fit in linear models

    Abstract: We consider a linear regression model and propose an omnibus test to simultaneously check the assumption of independence between the error and the predictor variables and the goodness of fit of the linear regression model.

  • March 16
    Time: 1:15–2:15 pm
    Speaker: Liping Gu
    Title: Analysis of the Dataset “Wine”

    Abstract:In the talk, I discuss the statistical analysis on the dataset “wine”. First I converted the numerical predictors into factorial ones and used the method which is similar to the factorial design to find the impact of different factors.Then making use of the algorithms of LDA and QDA I analyzed the data. The result of the computation shows a clear tendency on the means of the predictors that stand out as significant in the result of the factorial model. The data analysis suggests that 4 predictors are significant, whereas the other 7 are not.

  • May 2
    Time: 12–1 pm
    Speaker: Hao Wang
    Title: On testing independence and goodness of fit in linear models

    Abstract: We consider a linear regression model and propose an omnibus test to simultaneously check the assumption of independence between the error and the predictor variables and the goodness of fit of the linear regression model.

seminars/mas_capstone.txt · Last modified: 2017/09/20 15:15 by qyu