
A New Exact Confidence Interval for the Difference of Two Binomial Proportions
We consider interval estimation of the difference between two binomial p...
read it

A Unified Approach for Constructing Confidence Intervals and Hypothesis Tests Using hfunction
We introduce a general method, named the hfunction method, to unify the...
read it

The shortest confidence interval for the weighted sum of two Binomial proportions
Interval estimation of the probability of success in a Binomial model is...
read it

Exactcorrected confidence interval for risk difference in noninferiority binomial trials
A novel confidence interval estimator is proposed for the risk differenc...
read it

Asymptotic coverage probabilities of bootstrap percentile confidence intervals for constrained parameters
The asymptotic behaviour of the commonly used bootstrap percentile confi...
read it

Exact confidence interval for generalized FlajoletMartin algorithms
This paper develop a deep mathematicalstatistical approach to analyze a...
read it

The Shortest Confidence Interval for the Ratio of Quantiles of the Dagum Distribution
Jędrzejczak et al. (2018) constructed a confidence interval for a ratio ...
read it
Exact Confidence Intervals for Linear Combinations of Multinomial Probabilities
Linear combinations of multinomial probabilities, such as those resulting from contingency tables, are of use when evaluating classification system performance. While large sample inference methods for these combinations exist, small sample methods exist only for regions on the multinomial parameter space instead of the linear combinations. However, in medical classification problems it is common to have small samples necessitating a small sample confidence interval on linear combinations of multinomial probabilities. Therefore, in this paper we derive an exact confidence interval, through the use of fiducial inference, for linear combinations of multinomial probabilities. Simulation demonstrates the presented interval's adherence to exact coverage. Additionally, an adjustment to the exact interval is provided, giving shorter lengths while still achieving better coverage than large sample methods. Computational efficiencies in estimation of the exact interval are achieved through the application of a fast Fourier transform and combining a numerical solver and stochastic optimizer to find solutions. The exact confidence interval presented in this paper allows for comparisons between diagnostic methods previously unavailable, demonstrated through an example of diagnosing chronic allograph nephropathy in post kidney transplant patients.
READ FULL TEXT
Comments
There are no comments yet.