Statistical hypothesis testing

About: Statistical hypothesis testing is a research topic. Over the lifetime, 19580 publications have been published within this topic receiving 1037815 citations. The topic is also known as: statistical hypothesis testing & confirmatory data analysis.

Combining filtering and statistical methods for anomaly detection

Abstract: In this work we develop an approach for anomaly detection for large scale networks such as that of an enterprize or an ISP. The traffic patterns we focus on for analysis are that of a network-wide view of the traffic state, called the traffic matrix. In the first step a Kalman filter is used to filter out the "normal" traffic. This is done by comparing our future predictions of the traffic matrix state to an inference of the actual traffic matrix that is made using more recent measurement data than those used for prediction. In the second step the residual filtered process is then examined for anomalies. We explain here how any anomaly detection method can be viewed as a problem in statistical hypothesis testing. We study and compare four different methods for analyzing residuals, two of which are new. These methods focus on different aspects of the traffic pattern change. One focuses on instantaneous behavior, another focuses on changes in the mean of the residual process, a third on changes in the variance behavior, and a fourth examines variance changes over multiple timescales. We evaluate and compare all of these methods using ROC curves that illustrate the full tradeoff between false positives and false negatives for the complete spectrum of decision thresholds.

Your statistical consultant : answers to your data analysis questions

Abstract: PART ONE: GETTING STARTED WITH STATISTICAL ANALYSIS How Do I Prepare Data for Statistical Analysis? How Do I Examine Data Prior to Analysis? PART TWO: THE LOGIC OF STATISTICAL ANALYSIS: ISSUES REGARDING THE NATURE OF STATISTICS AND STATISTICAL TESTS Traditional Approaches to Statistical Analysis and the Logic of Statistical Inference Rethinking Traditional Paradigms Power, Effect Size and Hypothesis Testing Alternatives What Are the Assumptions of Statistical Testing? How Do I Select the Appropriate Statistical Test? PART THREE: ISSUES RELATED TO VARIABLES AND THEIR DISTRIBUTIONS How Do I Deal with Non-Normality, Missing Values and Outliers? Types of Variables and Their Treatment in Statistical Analysis PART FOUR: UNDERSTANDING THE BIG TWO: MAJOR QUESTIONS ABOUT ANALYSIS OF VARIANCE AND REGRESSION ANALYSIS Questions about Analysis of Variance Questions about Multiple Regression Analysis The Bigger Picture

Numerical estimation of survival rates from band-recovery and biotelemetry data

Abstract: The estimation of survival rates from tagging or banding data has been well developed by Brownie et al. However, problems occur when sparse data sets result in undefined estimates, when survival estimates exceed unity, when a hypothesis about the data cannot be tested by any of the available models, andwhen constraints on model estimators are desired. This paper presents a general analysis method whereby of the models Brownie et al. and many other methods described in the literature are merely special cases. Models are specified algebraically as cell probabilities consisting of functions of the survival rates and other parameters to be estimated. These algebraic expressions and the observed cell values are input to the computer program SURVIV to provide maximum-likelihood estimates of the unknown parameters and perform hypothesis tests on the data. The generality of the model specification also allows estimation of survival rates form biotelemetry data. 3 tables, 1 figure.

Rejecting or Accepting Parameter Values in Bayesian Estimation

Abstract: This article explains a decision rule that uses Bayesian posterior distributions as the basis for accepting or rejecting null values of parameters. This decision rule focuses on the range of plausible values indicated by the highest density interval of the posterior distribution and the relation between this range and a region of practical equivalence (ROPE) around the null value. The article also discusses considerations for setting the limits of a ROPE and emphasizes that analogous considerations apply to setting the decision thresholds for p values and Bayes factors.