In this study, we consider the test statistics that can be written as the sample average of data and derive their limiting distribution under the maximum likelihood (ML) and the quasi-maximum likelihood (QML) frameworks. We first generalize the asymptotic variance formula suggested in Pierce...

Almost all available statistical packages are capable of performing Analysis of Variance (ANOVA) from raw data. Some of statistical packages have capability to perform independent sample t-test, and some other tests of significance on summary data, but seldom would you come across a software...

Introduction to empirical data analysis -- Regression analysis -- Analysis of variance -- Discriminant analysis -- Logistic regression -- Contingency analysis -- Factor analysis -- Cluster analysis -- Conjoint analysis.

This chapter discusses the formal and informal techniques that are commonly used to give quantitative answers in the field of distributional analysis. To this end, it covers subjects including inequality, poverty, and the modeling of income distributions. It also deals with parametric and...

What is Statistics? Opinions vary. In fact, there is a continuous spectrum of attitudes toward statistics ranging from pure theoreticians, proving asymptotic efficiency and searching for most powerful tests, to wild practitioners, blindly reporting p-values and claiming statistical significance...

Let Y (formula) denote the nth (upper) k-record value of an infinite sequence of independent, identically distributed random variables with common continuous distribution function F. We show that if the nthk-record value Y (formula) has an increasing failure rate (IFR), then Y (formula) and Y...

As I document using evidence from a journal data repository that I manage, the datasets used in empirical work are getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues....

Estimation of the quantile model, especially with a large data set, can be computationally burdensome. This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model. The intuition of quantile coupling is to divide the original observations...