Practical Nonparametric Statistics, 3rd
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Now in its Third Edition, this classic text and reference book, written by a well-known Wiley author, is intended mainly for one-semester advanced undergraduate and undergrad/graduate introductory courses in nonparametric (or distribution free) statistics. The book will also appeal to applied research workers as a quick reference to the most useful nonparametric methods.
Purpose: To introduce the principles and applications of commonly used nonparametric methods. To compare these methods to their parametric counterparts through simulation studies. To introduce the basic methods for analyzing contingency tables.
Welcome Getting started What's new in this version Installing Analyse-it Starting Analyse-it Defining Datasets Setting Variable properties Running a statistical test Working with analysis reports Analyse-it Standard edition Describe Compare groups Summary statistics, Box/Dot/Mean plots Test Difference in Location Independent t-test Mann-Whitney test 1-way ANOVA 2-way ANOVA Kruskal-Wallis test Median test Test Difference in Dispersion Test Difference in Proportion Compare pairs Correlation Agreement Regression Analyse-it Method Evaluation edition Citing Analyse-it Contact us About us
Evaluation of cluster-based interventions presents significant methodological challenges. In this talk, we describe the design and analysis of the SEARCH trial, an ongoing community randomized trial to evaluate the impact of early HIV diagnosis and immediate treatment with streamlined care in rural Uganda and Kenya. We focus on 3 choices to optimize the design and analysis: causal parameter, pair-matching, and data-adaptive estimation. These choices are compared theoretically and with finite sample simulations. We demonstrate how each choice improves upon standard practice. We conclude with practical implications and some ongoing challenges.
In various practical settings of educational and psychological measurement, individuals are potentially selected according to their ability levels before being measured. In this case, understanding the selection process would shed light on either possible unexpected issues in the administration of the measurement or important features of the group of people being measured. Given such importance, we will explore the potential selection process in this research. Especially, we will build a nonparametric Bayesian model to account for a monotonic selection effect in item response theory (IRT), where individuals with higher ability are more likely to be measured. Simulation results show that this model is able to identify and recover the selection effect in the population.
We consider comparisons of statistical learning algorithms using multiple datasets, via leave-one-in cross-study validation: each of the algorithms is trained on one dataset; the resulting model is then validated on each remaining dataset. This poses two statistical challenges that need to be addressed simultaneously. The first is the assessment of study heterogeneity, with the aim of identifying subset of studies within which algorithm comparisons can be reliably carried out. The second is the comparison of algorithms using the ensemble of datasets. We address both problems by integrating clustering and model comparison. We formulate a Bayesian model for the array of cross-study validation statistics, which defines clusters of studies with similar properties, and provides the basis for meaningful algorithm comparison in the presence of study heterogeneity. We illustrate our approach through simulations involving studies with varying severity of systematic errors, and in the context of medical prognosis for patients diagnosed with cancer, using high-throughput measurements of the transcriptional activity of the tumor's genes.
The growing emphasis on patient-centered care has accelerated the demand for high quality data from patient reported outcome measures (i.e. quality of life, depression, physical functioning). Traditionally, the development and validation of these measures has been guided by Classical Test Theory. However, Item Response Theory, an alternate measurement framework, offers promise for addressing practical measurement problems found in health-related research that have been difficult to solve through Classical methods. This talk will introduce foundational concepts in Item Response Theory, as well as commonly used models and their assumptions. Example will be provided that exemplify typical applications of Item Response Theory. These examples will illustrate how Item Response Theory can be used to improve the development, refinement, and evaluation of patient reported outcome measures. Greater use of methods based on this framework can increase the accuracy and efficiency with which patient reported outcomes are measured.
This workshop uses practical examples to illustrate the use of probability sampling of RCT and population health studies. The approach is used to optimize the generalizability of, increase statistical power and add values to the collected data by preserving the possibility of sub-group analysis.
This workshop will discuss analytic approaches to situations where the sampling distribution of a variable is not known and cannot be assumed to be normal. Bootstrap resampling is a feasible alternative to conventional nonparametric statistics and can also be used to estimate the power of a comparison.
The course provides an introduction to the modelling of economic and management variables using regression and multivariate methods, both in a parametric than a nonparametric framework; the emphasis is on business, marketing and industrial applications. The program will cover models for the analysis of dependence (linear regression, ANOVA, autoregressive model, logit and probit models) and exploratory techniques for data reduction (principal component analysis and clustering analysis).
LEARNING OBJECTIVESThe course provides an introduction to modelling economic and management variables using regression and multivariate methods, both in a parametric and a nonparametric framework, with an emphasis on applications in business, marketing and industry. In particular, in the course are presented models for the analysis of dependence (linear regression, ANOVA, autoregressive model, logit and probit model,) and exploratory techniques for data reduction (principal component analysis and clustering analysis). 59ce067264
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