The effect of Monte Carlo approximation on coverage error of double-bootstrap confidence intervals

A double-bootstrap confidence interval must usually be approximated by a Monte Carlo simulation, consisting of two nested levels of bootstrap sampling. We provide an analysis of the coverage accuracy of the interval which takes account of both the inherent bootstrap and Monte Carlo errors. The analysis shows that, by a suitable choice of the number of resamples drawn at the inner level of bootstrap sampling, we can reduce the order of coverage error. We consider also the effects of performing a finite Monte Carlo simulation on the mean length and variability of length of two-sided intervals. An adaptive procedure is presented for the choice of the number of inner level resamples. The effectiveness of the procedure is illustrated through a small simulation study.     

Environmental effects on perceptions of life quality in Korea

Both in industrialized and industrializing countries environment is widely believed to be an important contributing source of the quality which people seek to bring to their lives. Yet, quality of life researchers have made little effort to determine the exact nature of environmental effects on perceptions of quality of life. This paper examines the individual and joint effects of community, housing and household on the levels of satisfaction, dissatisfaction, pleasure, and worry which Korean people experience.     

On the effect of correlation and unequal variances in detecting a spurious observation

Srivastava (1980) has shown that Grubbs's (1950) test for a univariate outlier is robust against the effect of equicorrelation. In this note we extend Srivastava's result by giving a more general covariance structure, which relaxes both the covariance structure and the assumption of equal variances. We also show that under the more general covariance structure, the power of Grubbs's test, as well as the significance level, is identical to the independently and identically distributed case.     

Young adults' knowledge of the strength of different alcoholic beverages

Providing information about the strength of different alcoholic beverages is a common component of alcohol abuse prevention programs. However, little is known about drinkers' knowledge of the strength of different alcoholic beverages. In the present study, 113 young adult drinkers responded to a questionnaire concerning the alcohol content of different types of malt beverages (beers, malt liquors), wines, fortified wines (port, sherry), and distilled spirits. The results indicated rates of correct responses well below 50 percent for each type of alcoholic beverage, with a substantial proportion of subjects either overestimating or underestimating alcohol content. Not a single subject reported correct alcohol content values for all four beverage types. There was a trend towards less accurate estimates of the alcohol content of strength were less accurate than those of men. The results are discussed in terms of prevention of alcohol abuse.     

The Goldschmidt Hypothesis in Chile1

Abstract The claim that large farms with hired labor undermine community institutions and reduce average welfare while family farms enhance these dimensions is tested in the context of Chile's expanding large-scale export agriculture. The expected negative effect of scale shows up statistically but then disappears when an appropriate regional control is introduced. However, further analysis reveals a negative rural production effect that withstands this control and sets the stage for a competitive test. The counter hypothesis explored rejects production organization of any size as causal, proposing instead that two dimensions of provincial structure-urban differentiation and pluralism-interact with efficient production organization, seen as an ad hoc factor, to determine welfare levels. A partial test of this structural mediation model explains much of the variance of infant mortality (the criterion of welfare). Scale has no effect.     

Simplicity out of complexity in environmental modelling: Occam's razor revisited

While large models based on a deterministic-reductionist philosophy have an important part to play in environmental research, it is advantageous to consider alternative modelling methodologies which overtly acknowledge the poorly defined and uncertain nature of most environmental systems. The paper discusses this topic and presents an integrated statistical modelling procedure which involves three main methodological tools: uncertainty and sensitivity studies based on Monte Carlo simulation techniques; dominant mode analysis using a new method of combined linearization and model-order reduction; and data-based mechanistic modelling. This novel approach is illustrated by two practical examples: modelling the global carbon cycle in relation to possible climate change; and modelling a horticultural glasshouse for the purposes of automatic climate control system design.     

Quasi-random resampling for the bootstrap

Quasi-random sequences are known to give efficient numerical integration rules in many Bayesian statistical problems where the posterior distribution can be transformed into periodic functions on then-dimensional hypercube. From this idea we develop a quasi-random approach to the generation of resamples used for Monte Carlo approximations to bootstrap estimates of bias, variance and distribution functions. We demonstrate a major difference between quasi-random bootstrap resamples, which are generated by deterministic algorithms and have no true randomness, and the usual pseudo-random bootstrap resamples generated by the classical bootstrap approach. Various quasi-random approaches are considered and are shown via a simulation study to result in approximants that are competitive in terms of efficiency when compared with other bootstrap Monte Carlo procedures such as balanced and antithetic resampling.