Abstract We propose a new method, the confidence interval (CI) to select valid instruments from larger set of potential for instrumental variable (IV) estimation causal effect an exposure on outcome. Invalid are such that they fail exclusion conditions and enter model as explanatory variables. The CI method is based CIs per instrument effects estimates selects largest group with all overlapping...

Two approaches for dealing with the problem of poor coverage probabilities of certain standard confidence intervals are proposed. The first is a recommendation that the actual coverage be estimated irectly from the data and its value reported in addition to the nominal level. This is achieved through a combination of computer simulation and density estimation. The asymptotic validity of the pro...

This paper presents an improved result on the negative-binomial Monte Carlo technique analyzed in a previous paper for the estimation of an unknown probability p. Specifically, the confidence level associated to a relative interval [p/μ2, pμ1], with μ1, μ2 > 1, is proved to exceed its asymptotic value for a broader range of intervals than that given in the referred paper, and for any value of p...

Performances of some common confidence interval construction procedures for direct standardized incidence or mortality rate were compared. The asymptotic method, the method based on matching moments, the method based on gamma distribution and the method based on beta distribution were investigated through Monte Carlo simulations based on data from an epidemiologic study of cardiovascular diseas...

Existing methods for setting confidence intervals for the difference theta between binomial proportions based on paired data perform inadequately. The asymptotic method can produce limits outside the range of validity. The 'exact' conditional method can yield an interval which is effectively only one-sided. Both these methods also have poor coverage properties. Better methods are described, bas...

Log-concave density estimation is considered as an alternative for kernel density estimations which does not depend on tuning parameters. Pointwise asymptotic theory has been already developed for the nonparametric maximum likelihood estimator of a log-concave density. Here, the practical aspects of this theory are studied. In order to obtain a confidence interval, estimators of the constants a...