Quantifying the Effects of Modeling Simplifications for Structural Identification of Bridges

Several long-span, prestressed, segmental box girder bridges were built in the early 1980s and many of them are affected by long-term residual deformations. Although some models have been proposed to describe their structural behavior, several uncertainties remain. This paper examines the effects of errors introduced by model simplifications on predicted values. The results are used to improve the estimation of parameter values using model-based data-interpretation strategies. The procedure is illustrated for the Grand-Mere Bridge located in Canada. This bridge is affected by excessive long-term vertical displacements. Model simplifications such as in its degree of complexity are found to have an important influence on prediction errors. Representing these errors by zero-mean independent Gaussian noise does not adequately describe the relationships among the errors observed in this case study. Estimated errors are used in the interpretation of the ambient vibration acceleration data recorded on the structure. The interpretation approach employed is based on error-domain model falsification. This study provides ranges of parameter values that can be used subsequently to characterize more accurately aspects such as long-term creep and shrinkage behavior. (C) 2014 American Society of Civil Engineers

Classes of Multiple Decision Functions Strongly Controlling FWER and FDR

This paper provides two general classes of multiple decision functions where each member of the first class strongly controls the family-wise error rate (FWER), while each member of the second class strongly controls the false discovery rate (FDR). These classes offer the possibility that an optimal multiple decision function with respect to a pre-specified criterion, such as the missed discovery rate (MDR), could be found within these classes. Such multiple decision functions can be utilized in multiple testing, specifically, but not limited to, the analysis of high-dimensional microarray data sets.Comment: 19 page

Performance-Driven Measurement System Design for Structural Identification

Much progress has been achieved in the research field of structural identification, which is attributable to a better understanding of uncertainties, improvement in sensor technologies, and cost reductions. However, data interpretation remains a bottleneck. Too often, too much data are acquired, which hinders interpretation. In this paper, the writers describe a methodology that explicitly indicates when instrumentation can hinder the ability to interpret data. The approach includes uncertainties and dependencies that may affect model predictions. The writers use two performance indices to optimize measurement system designs, i.e.,monitoring costs and expected identification performance. A case study shows that the approach is able to justify a reduction in monitoring costs of 50% compared with an initial measurement configuration

Interim analyses of data as they accumulate in laboratory experimentation

BACKGROUND: Techniques for interim analysis, the statistical analysis of results while they are still accumulating, are highly-developed in the setting of clinical trials. But in the setting of laboratory experiments such analyses are usually conducted secretly and with no provisions for the necessary adjustments of the Type I error-rate. DISCUSSION: Laboratory researchers, from ignorance or by design, often analyse their results before the final number of experimental units (humans, animals, tissues or cells) has been reached. If this is done in an uncontrolled fashion, the pejorative term 'peeking' has been applied. A statistical penalty must be exacted. This is because if enough interim analyses are conducted, and if the outcome of the trial is on the borderline between 'significant' and 'not significant', ultimately one of the analyses will result in the magical P = 0.05. I suggest that Armitage's technique of matched-pairs sequential analysis should be considered. The conditions for using this technique are ideal: almost unlimited opportunity for matched pairing, and a short time between commencement of a study and its completion. Both the Type I and Type II error-rates are controlled. And the maximum number of pairs necessary to achieve an outcome, whether P = 0.05 or P > 0.05, can be estimated in advance. SUMMARY: Laboratory investigators, if they are to be honest, must adjust the critical value of P if they analyse their data repeatedly. I suggest they should consider employing matched-pairs sequential analysis in designing their experiments

MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS

The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes

A P-value model for theoretical power analysis and its applications in multiple testing procedures

Background: Power analysis is a critical aspect of the design of experiments to detect an effect of a given size. When multiple hypotheses are tested simultaneously, multiplicity adjustments to p-values should be taken into account in power analysis. There are a limited number of studies on power analysis in multiple testing procedures. For some methods, the theoretical analysis is difficult and extensive numerical simulations are often needed, while other methods oversimplify the information under the alternative hypothesis. To this end, this paper aims to develop a new statistical model for power analysis in multiple testing procedures. Methods: We propose a step-function-based p-value model under the alternative hypothesis, which is simple enough to perform power analysis without simulations, but not too simple to lose the information from the alternative hypothesis. The first step is to transform distributions of different test statistics (e.g., t, chi-square or F) to distributions of corresponding p-values. We then use a step function to approximate each of the p-value’s distributions by matching the mean and variance. Lastly, the step-function-based p-value model can be used for theoretical power analysis. Results: The proposed model is applied to problems in multiple testing procedures. We first show how the most powerful critical constants can be chosen using the step-function-based p-value model. Our model is then applied to the field of multiple testing procedures to explain the assumption of monotonicity of the critical constants. Lastly, we apply our model to a behavioral weight loss and maintenance study to select the optimal critical constants. Conclusions: The proposed model is easy to implement and preserves the information from the alternative hypothesis

The Neolithic Demographic Transition in Europe: Correlation with Juvenility Index Supports Interpretation of the Summed Calibrated Radiocarbon Date Probability Distribution (SCDPD) as a Valid Demographic Proxy

Analysis of the proportion of immature skeletons recovered from European prehistoric cemeteries has shown that the transition to agriculture after 9000 BP triggered a long-term increase in human fertility. Here we compare the largest analysis of European cemeteries to date with an independent line of evidence, the summed calibrated date probability distribution of radiocarbon dates (SCDPD) from archaeological sites. Our cemetery reanalysis confirms increased growth rates after the introduction of agriculture; the radiocarbon analysis also shows this pattern, and a significant correlation between both lines of evidence confirms the demographic validity of SCDPDs. We analyze the areal extent of Neolithic enclosures and demographic data from ethnographically known farming and foraging societies and we estimate differences in population levels at individual sites. We find little effect on the overall shape and precision of the SCDPD and we observe a small increase in the correlation with the cemetery trends. The SCDPD analysis supports the hypothesis that the transition to agriculture dramatically increased demographic growth, but it was followed within centuries by a general pattern of collapse even after accounting for higher settlement densities during the Neolithic. The study supports the unique contribution of SCDPDs as a valid demographic proxy for the demographic patterns associated with early agriculture