Population,Food and Economic Adjustment

This article investigates the number of games of baseball that should be played (1) in a World Series competition, and (2) in a pennant race competition within each league, in order to have a reasonable level of confidence that the best team wins the competition. The current number of games played is found to be highly inadequate for the World Series and only barely sufficient for the pennant race.     

A simple procedure for testing linear hypotheses about the parameters of a nonlinear model using weighted least squares

Suppose the same nonlinear function involving k parameters is fit to each of t populations. Suppose further it is of interest to compare a specific parameter of the models across the populations. Such comparisons can be expressed as linear hypotheses about the parameters of the nonlinear models. A weighted linear least squares (WLLS) procedure is proposed to test these linear hypotheses. The advantages and disadvantages of the WLLS procedure are discussed. This procedure is also compared to a nonlinear least squares procedure for testing these hypotheses in nonlinear models.     

Exact confidence regions for scale and location parameters based on sample quartiles

In this paper we present relatively simple (ruler, paper, and pencil) nonparametric procedures for constructing joint confidence regions for (i) the median and the inner quartile range for the symmetric one-sample problem and (ii) the shift and ratio of scale parameters for the two-sample case. Both procedures are functions of the sample quartiles and have exact confidence levels when the populations are continuous. The one-sample case requires symmetry of first and third quartiles about the median.

The confidence regions we propose are always convex, nested for decreasing confidence levels and are compact for reasonably large sample sizes. Both exact small sample and approximate large sample distributions are given.     

Admissible minimax model selection rule

This paper considers the problem of choosing one between the simple model N(0, I_d) and the full model N(0 I_d) based on the observation X from N(θ I_d) where X, θεR^d, 0 is the null vector in R^d and I_d is the d×d identity matrix. It is shown that the selection rule which chooses the full model if |x| > a_o , for some a₀ > 0 and the simple model otherwise is an admissible minimax model selection rule relative to a loss function which takes into account both inaccuracy and complexity.     

Bayesian inference and prediction of order statistics for a Type-II censored Weibull distribution

This paper describes the Bayesian inference and prediction of the two-parameter Weibull distribution when the data are Type-II censored data. The aim of this paper is twofold. First we consider the Bayesian inference of the unknown parameters under different loss functions. The Bayes estimates cannot be obtained in closed form. We use Gibbs sampling procedure to draw Markov Chain Monte Carlo (MCMC) samples and it has been used to compute the Bayes estimates and also to construct symmetric credible intervals. Further we consider the Bayes prediction of the future order statistics based on the observed sample. We consider the posterior predictive density of the future observations and also construct a predictive interval with a given coverage probability. Monte Carlo simulations are performed to compare different methods and one data analysis is performed for illustration purposes.     

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.     

Gaussian process emulation for second-order Monte Carlo simulations

We consider the use of emulator technology as an alternative method to second-order Monte Carlo (2DMC) in the uncertainty analysis for a percentile from the output of a stochastic model. 2DMC is a technique that uses repeated sampling in order to make inferences on the uncertainty and variability in a model output. The conventional 2DMC approach can often be highly computational, making methods for uncertainty and sensitivity analysis unfeasible. We explore the adequacy and efficiency of the emulation approach, and we find that emulation provides a viable alternative in this situation. We demonstrate these methods using two different examples of different input dimensions, including an application that considers contamination in pre-pasteurised milk.     

Stakes and chips

Gambling has provided centuries of inspiration to probabilists and statisticians. The process continues. There also exist fundamental links between betting and a newer subject, Information Theory, which began with Claude Shannon and his ground-breaking 1948 paper A Mathematical Theory of Communication.
It is a result which arises very naturally. A successful gambler and a successful data compression algorithm must both accurately estimate probability distributions. There are practical results as well as theoretical: Shannon and colleagues, including the legendary gambler and mathematician Edward Thorp, actually attempted to apply results from information theory in Las Vegas casinos and stock market transactions. Oliver Johnson examines the connections.     

Comparison of Poverty and Income Disparity of Single Mothers and Fathers Across Three Decades: 1990–2010

As the potential for more children being raised by single parents increases, so does the societal need to examine this phenomena of single parent earnings and the impact it will have on the ability to support a family above the poverty line. Research suggests a substantial pay gap between men and women, but most research is limited to individuals in traditional families. This study explores income disparity and poverty between single mothers and single fathers across three decades (1990–2010), using a US nationally representative sample. Based on human capital theory, our analysis reveals that single mothers were more likely to be in poverty at far greater rates than single fathers, after controlling for a host of demographic, human capital, and work related variables. We also found that a contributing factor to this disparity is that single mothers were penalized for having more children while single fathers were not. We find that gendered poverty and the gender pay gap narrowed between 1990 and 2000, but have stayed stable since. Overall, human capital decreases the gender income and poverty gap, but a substantial gap still remains. Implications for policy-makers are discussed.