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26 Result(s)
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Jeffrey S. Simonoff
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Chapter and Conference Paper
The Potential for Nonparametric Joint Latent Class Modeling of Longitudinal and Time-to-Event Data
Joint latent class modeling (JLCM) of longitudinal and time-to-event data is a parametric approach of particular interest in clinical studies. JLCM has the flexibility to uncover complex data-dependent latent ...
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Article
Effect Coding as a Mechanism for Improving the Accuracy of Measuring Students Who Self-Identify with More than One Race
The purpose of this paper is to describe effect coding as an alternative quantitative practice for analyzing and interpreting categorical, multi-raced independent variables in higher education research. Not o...
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Article
Exploring Innovative Entrepreneurship and Its Ties to Higher Educational Experiences
The purpose of this paper was to explore innovative entrepreneurship and to gain insight into the educational practices and experiences that increase the likelihood that a student would graduate with innovativ...
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Article
RE-EM trees: a data mining approach for longitudinal and clustered data
Longitudinal data refer to the situation where repeated observations are available for each sampled object. Clustered data, where observations are nested in a hierarchical structure within objects (without tim...
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Book
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Chapter
Introduction
Students whose complete exposure to statistical methods comes from an introductory statistics class can easily get the impression that, with few exceptions, data analysis is all about examining continuous data...
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Chapter
Regression Models for Binary Data
The loglinear models of the previous four chapters are designed for count data, where a Poisson or multinomial distribution is appropriate. The most basic form of categorical data, however, is binary — 0 or 1....
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Chapter
Gaussian-Based Data Analysis
In the next two chapters we examine univariate and regression analysis based on the central distribution of statistical inference and data analysis, the normal, or Gaussian, distribution. It is important to no...
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Chapter
Categorical Data and Goodness-of-Fit
This chapter covers the building blocks of the analysis of categorical data. First we discuss the important random variables that are the basis of analysis — the binomial, Poisson, and multinomial distributions.
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Chapter
Analyzing Two-Way Tables
The count regression models of Chapter 5 are general enough to be applied in situations where predictors are categorical, which leads to data in the form of tables of counts, or contingency tables. Despite this, ...
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Chapter
Multidimensional Contingency Tables
Cross-classifications involving more than two variables are a natural generalization of the tables discussed in Chapters 6 and 7. From the generalized linear model (Poisson regression) point of view, this mere...
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Chapter
Regression Models for Multiple Category Response Data
The generalization of categorical data from two categories (the binomial random variable) to multiple categories (the multinomial random variable) is a fundamental step in the analysis of contingency tables, a...
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Chapter
Gaussian-Based Model Building
In this chapter we build on the material of the previous chapter, extending the model-building and model-checking capabilities of the least squares linear regression model. Most of this material typically is n...
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Chapter
Regression Models for Count Data
In Chapters 2 and 3 we explored the use of least squares regression to analyze and understand the relationship between a target variable and at least one predicting variable. While it was possible to model the...
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Chapter
Tables with More Structure
The hypothesis of independence is often just a straw man we — don’t really think that two factors are independent of each other, so rejection of independence isn’t very interesting. What would be more promisin...
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Book
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Chapter
Introduction
One thing that sets statisticians apart from other scientists is the general public’s relative ignorance about what the field of statistics actually is. People have at least a general idea of what chemistry or...
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Chapter
Smoother Univariate Density Estimation
The simple density estimators of Chapter 2 are informative, but they suffer from two serious drawbacks: they are not smooth, and they are not sensitive enough to local properties of f. It is easy to solve both of...
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Chapter
Nonparametric Regression
The most widely used general statistical procedure is (linear) regression. Regression models are powerful tools for modeling a target variable y as a function of a set of predictors x, allowing prediction for fut...
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Chapter
Further Applications of Smoothing
The focus of the previous chapters was mostly on the uses of smoothing as an exploratory tool in graphical data analysis. This chapter gives several (brief) examples of the application of smoothing methods in ...
