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Mean and Median Bias Reduction: A Concise Review and Application to Adjacent-Categories Logit Models
The estimation of categorical response models using bias-reducing adjusted score equations has seen extensive theoretical research and applied use....
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Integration of model-based recursive partitioning with bias reduction estimation: a case study assessing the impact of Oliver’s four factors on the probability of winning a basketball game
In this contribution, we investigate the importance of Oliver’s Four Factors, proposed in the literature to identify a basketball team’s strengths...
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Matrix-variate generalized linear model with measurement error
Matrix-variate generalized linear model (mvGLM) has been investigated successfully under the framework of tensor generalized linear model, because...
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Mean and median bias reduction in generalized linear models
This paper presents an integrated framework for estimation and inference from generalized linear models using adjusted score equations that result in...
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Considerations and Targeted Approaches to Identifying Bad Actors in Exposure Mixtures
Variable importance is a key statistical issue in exposure mixtures, as it allows a ranking of exposures as potential targets for intervention, and...
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Artificial systematic attenuation in eta squared and some related consequences: attenuation-corrected eta and eta squared, negative values of eta, and their relation to Pearson correlation
In general linear modeling (GLM), eta squared ( η 2 ) is the dominant statistic for the explaining power of an independent variable. This article...
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Advances in Maximum Likelihood Estimation of Fixed-Effects Binary Panel Data Models
We review recent fixed-effects approaches to the formulation and estimation of models for binary panel data, measured at T time occasions. We offer a...
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A Robust Hurdle Poisson Model in the Estimation of the Extremal Index
In statistical extreme value theory, the occurrence of clusters of exceedances above a high threshold is related to the extremal index (EI), when...
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Functional Magnetic Resonance Imaging
Functional Magnetic Resonance Imaging (fMRI) maps brain activity by detecting changes in image intensity related to neural activity by the blood...
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The Statistics of Machine Learning
This chapter offers a general introduction to the statistics of Machine Learning (ML) and constitutes the basics to get through the next chapters of...
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Statistical framework
Research questions. This chapter concerns the characteristics of research questions, data designs, and the more common multivariate statistical...
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Model Selection and Regularization
This chapter presents regularization and selection methods for linear and nonlinear (parametric)Parametric models. These are important Machine...
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Improved wrong-model inference for generalized linear models for binary responses in the presence of link misspecification
In the framework of generalized linear models for binary responses, we develop parametric methods that yield estimators for regression coefficients...
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More Than One Response Variable: Multivariate Analysis
Recall that the type of regression model you use is determined mostly by the properties of the response variable. Well what if you have more than one...
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Sarbanes–OxleyEngagements
Academic research on reporting from Sarbanes-Oxley Act of 2002 (SOX) has to date focused on internal consistency, compliance, and accrual accounting...
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Time Series II
This chapter continues the empirical analysis of the central England daily temperature series using Fourier techniques indexed by frequencies. The...
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Robust and efficient estimation of nonparametric generalized linear models
Generalized linear models are flexible tools for the analysis of diverse datasets, but the classical formulation requires that the parametric...
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Resampling Methods
Resampling methods are an indispensable tool in modern statistics. They involve repeatedly drawing samples from a training set and refitting a model...
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Analysis of Accounting Transactions
Accounting transactions are the “raw data” of accounting system, but the idiosyncratic vernacular of accounting, and spotty empirical study of...
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A Note on Robust Estimation of the Extremal Index
Many examples in the most diverse fields of application show the need for statistical methods of analysis of extremes of dependent data. A crucial...