Mathematical Statistics for Economics and Business

By R. Mittelhammer. 1996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (2016, background): “Next we present several known properties of almost sure convergence (Mittelhammer, 1996, Section 5.5).”
Regression analysis of variates observed on (0, 1): percentages, proportions and fractions (2003, methodology): “Based upon these comparisons, we recommend that researchers in the future use either a parametric regression model based upon the beta distribution or the quasilikelihood model developed by Papke and Wooldridge (1997). Concerning the choice between the parametric and quasi-likelihood model, we recom”
Impacts of meat product recalls on consumer demand in the USA (2004, methodology): “Asymptotic t-statistics below estimates are calculated as linear combinations of normal random variables (Theorem 4.9, Mittelhammer, 1996).”
Estimating Average Causal Effects Under General Interference (2012, methodology): “This model has been the workhorse for most causal effects estimation under the Neyman (1923)-Rubin paradigm.”
Multidimensional Item Response Theory Modeling of Binary Data: Large Sample Properties of NOHARM Estimates (2001, methodology): “The expression for i is obtained as This result follows from E[yi] = 1 Pr( yi = 1) + 0 Pr(yi = 0) = Pr(yi = 1), the double expectation theorem (e.g., Mittelhammer, 1996), Eη{E[yi ]} = E[yi], the expression for E [yi ] = Pr(yi = 1 ) given by (10), and the properties of normalized Hermite polynomials…”