The $\beta$-Model—Maximum Likelihood, Cramér–Rao Bounds, and Hypothesis Testing

Empirical likelihood based hypothesis testing

Omnibus tests for various nonparametric hypotheses are developed using the empirical likelihood method. These include tests for symmetry about zero, changes in distribution, independence and exponentiality. The approach is to localize the empirical likelihood using a suitable ‘time’ variable implicit in the null hypothesis and then form an integral of the log-likelihood ratio statistic. The asy...

Hypothesis Testing for Arbitrary Bounds

Maximum Likelihood Estimation and Hypothesis Testing

This chapter is a brief introduction to two important statistical methods— maximum likelihood estimation and hypothesis testing. We shall show how to use these methods to test the biological sequence models developed in previous chapters against experimental data. We shall also show how hypothesis testing ideas inspire scoring methods for sequence alignment. denote the outcome of an experiment ...

Maximum Likelihood Estimation and Hypothesis Testing

This chapter is a brief introduction to two important statistical methods— maximum likelihood estimation and hypothesis testing. We shall show how to use these methods to test the biological sequence models developed in previous chapters against experimental data. We shall also show how hypothesis testing ideas inspire scoring methods for sequence alignment. denote the outcome of an experiment ...

Robust Hypothesis Testing via Lq-Likelihood

This article introduces a robust hypothesis testing procedure: the Lq-likelihoodratio-type test (LqRT). By deriving the asymptotic distribution of this test statistic, the authors demonstrate its robustness both analytically and numerically, and they investigate the properties of both its influence function and its breakdown point. A proposed method to select the tuning parameter q offers a goo...