Principles of Statistical Inference from a Neo-Fisherian Perspective

Pace, Luigi; Salvan, Alessandra

doi:10.1142/3409

Cited by 129 publications

(80 citation statements)

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“…α and β, respectively. Based on this transformation (Pace and Salvan, 1997), for a given allele the transformed η obs value and central moments of η for its synonyms are,…”

Section: Skewness Reducing Transformation Of η Distributionmentioning

confidence: 99%

Measuring and Detecting Molecular Adaptation in Codon Usage Against Nonsense Errors During Protein Translation

2009

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Codon usage bias (CUB) has been documented across a wide range of taxa and is the subject of numerous studies. While most explanations of CUB invoke some type of natural selection, most measures of CUB adaptation are heuristically defined. In contrast, we present a novel and mechanistic method for defining and contextualizing CUB adaptation to reduce the cost of nonsense errors during protein translation. Using a model of protein translation, we develop a general approach for measuring the protein production cost in the face of nonsense errors of a given allele as well as the mean and variance of these costs across its coding synonyms. We then use these results to define the nonsense error adaptation index (NAI) of the allele or a contiguous subset thereof. Conceptually, the NAI value of an allele is a relative measure of its elevation on a specific and well-defined adaptive landscape. To illustrate its utility, we calculate NAI values for the entire coding sequence and across a set of nonoverlapping windows for each gene in the Saccharomyces cerevisiae S288c genome. Our results provide clear evidence of adaptation to reduce the cost of nonsense errors and increasing adaptation with codon position and expression. The magnitude and nature of this adaptation are also largely consistent with simulation results in which nonsense errors are the only selective force driving CUB evolution. Because NAI is derived from mechanistic models, it is both easier to interpret and more amenable to future refinement than other commonly used measures of codon bias. Further, our approach can also be used as a starting point for developing other mechanistically derived measures of adaptation such as for translational accuracy.

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“…α and β, respectively. Based on this transformation (Pace and Salvan, 1997), for a given allele the transformed η obs value and central moments of η for its synonyms are,…”

Section: Skewness Reducing Transformation Of η Distributionmentioning

confidence: 99%

Measuring and Detecting Molecular Adaptation in Codon Usage Against Nonsense Errors During Protein Translation

2009

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“…Improved likelihood inferences may be obtained through higher order asymptotics, on which there is a large literature summarized in books by Barndorff-Nielsen and Cox (1994), Pace and Salvan (1997), Severini (2000), and Brazzale, Davison, and Reid (2007), and in review articles such as Reid (2003). One basic formula is the so-called p * approximation to the density of the maximum likelihood estimator conditioned on an ancillary statistic (Barndorff-Nielsen 1980, 1983, 1986, manipulation of which yields the modified likelihood root…”

Section: Higher Order Approximationsmentioning

confidence: 99%

Three Examples of Accurate Likelihood Inference

Lozada-Can

Davison

2010

The American Statistician

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The modern theory of likelihood inference provides improved inferences in many parametric models, with little more effort than is required for application of standard first-order theory. We outline the relevant computations, and illustrate the calculations using a dilution assay, a zero-inflated Poisson regression model, and a short time series. In each case the effect of the higher order correction can be appreciable.

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“…. , a n ), with a i = (y i −μ)/θ: see, for example, Pace and Salvan [24,Section 7.6]. Again, we perform a simulation of 5000 datasets, for various sample sizes n, from the Weibull density with ν = λ = 1, and consider both one-sided and two-sided testing of the hypothesis H 0 : θ = 1, in the one-sided case testing against θ > 1.…”

Section: Weibull Distributionmentioning

confidence: 99%

Computer-intensive conditional inference

Young

DiCiccio

2010

Contributions to Statistics

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Abstract. Conditional inference is a fundamental part of statistical theory. However, exact conditional inference is often awkward, leading to the desire for methods which offer accurate approximations. Such a methodology is provided by small-sample likelihood asymptotics. We argue in this paper that simple, simulation-based methods also offer accurate approximations to exact conditional inference in multiparameter exponential family and ancillary statistic settings. Bootstrap simulation of the marginal distribution of an appropriate statistic provides a conceptually simple and highly effective alternative to analytic procedures of approximate conditional inference.

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Principles of Statistical Inference from a Neo-Fisherian Perspective

Cited by 129 publications

References 0 publications

Measuring and Detecting Molecular Adaptation in Codon Usage Against Nonsense Errors During Protein Translation

Measuring and Detecting Molecular Adaptation in Codon Usage Against Nonsense Errors During Protein Translation

Three Examples of Accurate Likelihood Inference

Computer-intensive conditional inference

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