Estimates of Lifetime Risk of Occupational Fatal Injury from Age-Specific Rates

The lifetime risk of fatal workplace injury is a critical issue in the evaluation of occupational hazards. Recently, Fosbroke, Kisner, and Myers (1997) described a metric for working lifetime risk (WLTR) to determine the probability that a worker will die due to a work-related fatal injury in a year over a certain number of years of employment. This quantity was defined assuming that the annual rate of fatal injuries will be the same each year during employment. Recognizing the fact that annual fatal injury rates differ with the age of the worker along with other factors, modification of the definition of working lifetime risk is derived. We obtain the estimates of the lifetime risk using age-categorized annual fatality rates and derive an estimate of the standard error of the WLTR estimator and a confidence interval for the WLTR. We illustrate these calculations by estimating the lifetime risk for work-related fatal injuries for workers in four high risk industries: agriculture-forestry-fishing, mining, construction, and transportation public utilities. The estimates are based on employment data from the Bureau of Labor Statistics and an updated version of fatality data from the National Traumatic Occupational Fatalities surveillance system.     

The Use of a Symbol Processing Computer Language in Point Estimation of Parameters and in Construction of Confidence Intervals

This paper presents a method to generate automatically computer programs which are necessary for parameter estimation, hypothesis tests and construction of confidence intervals by the maximum likelihood method. The spectral or density function of the random variable is arbitrary, but must be known and given in closed form. The programming language used is the symbol processing language LIBAFORM, whose statements are interpreted by a package of LISP-routines. The application of the method is illustrated by the analysis of a linear model whose residuals follow a logarithmic F-distribution, and the analysis of a dose-response curve.     

A Comparison of the MPN and Fisher-Yates Estimators for the Density of Organisms

The method of moments estimator, discussed in FISHER and YATES (1970), for the density of bacteria is compared with the maximum likelihood or MPN estimator: For sample sizes used in practice the estimators are found to be very similar. Estimators which reduce bias are discussed and their use recommended.     

A Confidence‐Limit‐Based Approach to the Assessment of Hardy–Weinberg Equilibrium

The classical χ²‐procedure for the assessment of Hardy–Weinberg equilibrium (HWE) is tailored for detecting violations of HWE. However, many applications in genetic epidemiology require approximate compatibility with HWE. In a previous contribution to the field (Wellek, S. (2004). Biometrics, 60 , 694–703), the methodology of statistical equivalence testing was exploited for the construction of tests for problems in which the assumption of approximate compatibility of a given genotype distribution with HWE plays the role of the alternative hypothesis one aims to establish. In this article, we propose a procedure serving the same purpose but relying on confidence limits rather than critical bounds of a significance test. Interval estimation relates to essentially the same parametric function that was previously chosen as the target parameter for constructing an exact conditional UMPU test for equivalence with a HWE conforming genotype distribution. This population parameter is shown to have a direct genetic interpretation as a measure of relative excess heterozygosity. Confidence limits are constructed using both asymptotic and exact methods. The new approach is illustrated by reanalyzing genotype distributions obtained from published genetic association studies, and detailed guidance for choosing the equivalence margin is provided. The methods have been implemented in freely available SAS macros.     

Robustness of Statistical Methods II. Methods for the one-Sample Problem

This paper presents a new exact method of the investigation of the robustness in the one-sample-case of the u-, t- and χ²-statistics for discrete alternatives to the underlying normal distribution. First results for sample sizes up to n = 230 are given.     

Constructing confidence intervals for selected parameters

In large-scale problems, it is common practice to select important parameters by a procedure such as the Benjamini and Hochberg procedure and construct confidence intervals (CIs) for further investigation while the false coverage-statement rate (FCR) for the CIs is controlled at a desired level. Although the well-known BY CIs control the FCR, they are uniformly inflated. In this paper, we propose two methods to construct shorter selective CIs. The first method produces shorter CIs by allowing a reduced number of selective CIs. The second method produces shorter CIs by allowing a prefixed proportion of CIs containing the values of uninteresting parameters. We theoretically prove that the proposed CIs are uniformly shorter than BY CIs and control the FCR asymptotically for independent data. Numerical results confirm our theoretical results and show that the proposed CIs still work for correlated data. We illustrate the advantage of the proposed procedures by analyzing the microarray data from a HIV study.     

Categorical data analysis in experimental biology

The categorical data set is an important data class in experimental biology and contains data separable into several mutually exclusive categories. Unlike measurement of a continuous variable, categorical data cannot be analyzed with methods such as the Student's t-test. Thus, these data require a different method of analysis to aid in interpretation. In this article, we will review issues related to categorical data, such as how to plot them in a graph, how to integrate results from different experiments, how to calculate the error bar/region, and how to perform significance tests. In addition, we illustrate analysis of categorical data using experimental results from developmental biology and virology studies.     

Diminished Confidence Levels in the Interval Estimation of an Unknown Regressor in Model I of Linear Regression

For the model y=β₀ + β₁ x + e (model I of linear regression) in the literature confidence estimators of an unknown position x₀ are given at which either the expectation of y is given (see FIELLER, 1944; FINNEY, 1952), or realizations of y are given (see GRAYBILL, 1961). These confidence regions with level 1—α need not be intervals. The occurrence of interval shape is a random event. Its probability is equal to the power of the t test for the examination of the hypothesis H: β₁ = 0. The papers mentioned above claim to provide confidence intervals with level 1 ? α. But because of the restriction of (1 —α)—confidence regions to intervals the true confidence probability is the conditional probability W_c: W_c = P (the confidence region covers x₀| the region has interval shape). Here this conditional probability is shown to be less than 1 —α. Evidence on the possible deviations from 1 —α has been obtained by simulations.     

Application of phylogenetic networks in evolutionary studies

The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.     

R法遗传参数估值置信区间的计算方法和重复估计次数的研究

探讨R法遗传参数估值置信区间的计算方法和重复估计次数(NORE)对参数估值的影响,利用4种模型通过模拟产生数据集。基础群中公、母畜数分别为200和2000头,BLUP育种值选择5个世代。利用多变量乘法迭代(MMI)法,结合先决条件的共扼梯度(PCG)法求解混合模型方程组估计方差组分。用经典方法、Box-Cox变换后的经典方法和自助法计算参数估值的均数、标准误和置信区间。结果表明,重复估计次数较多时,3种方法均可;重复估计次数较少时,建议使用自助法。简单模型下需要较少的重复估计,但对于复杂模型则需要较多的重复估计。随模型中随机效应数的增加,直接遗传力高估。随着PCG和MMI轮次的增大,参数估值表现出低估的趋势。     

A confidence interval for the unknown value of the regressor with given expectation of the regressand in model I of linear regression

In the case of model I of linear regression there is derived a confidence interval for that x_o where the “true line” will reach a given value y_o. The interval can be given by the intersections between the line y = y_o and the hyperbolas providing pointwise confidence intervals of the expectations of y.     

The estimation and applicability of confidence intervals for Stander's Similarity Index (SIMI) in algal assemblage comparisons

Stander's Similarity Index (SIMI) has become a popular measure for comparing algal assemblages. The interpretation of the value produced by this index, however, has been highly variable between studies. Using replicate sampling of natural algal assemblages and computer simulation techniques, a method by which confidence intervals could be applied to SIMI was developed. This paper presents that method and gives an example using hypothetical species counts of two algal assemblages. Since a more realistic range of the actual similarity between two assemblages is produced, the estimation of confidence intervals when using SIMI is recommended.     

Perceived mate fidelity and paternal resemblance predict men's investment in children

Here, we investigate whether variation in male parental investment can be explained in terms of (1) men's perception of the degree of resemblance between themselves and their offspring and (2) men's perception of their mates' fidelity. In a sample of men from London's Heathrow airport, both variables were found to predict reported investment. We also examined whether the predictors of investment varied when men were no longer in a relationship with the mother of their children and are therefore no longer investing in mating effort with them. Among men no longer in a relationship with the mother of their children, resemblance became a stronger predictor of investment, while fidelity was no longer a significant predictor. Overall, men provided less investment to their children if they were no longer in a relationship with the mother of their children.