**Likelihood Functions**: Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.

**Bayes Theorem**: A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.

**Signal-To-Noise Ratio**: The comparison of the quantity of meaningful data to the irrelevant or incorrect data.

**Algorithms**: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.

**Computer Simulation**: Computer-based representation of physical systems and phenomena such as chemical processes.

**Models, Statistical**: Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.

**Markov Chains**: A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.

**Models, Genetic**: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**Monte Carlo Method**: In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)

**Probability**: The study of chance processes or the relative frequency characterizing a chance process.

**Data Interpretation, Statistical**: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.

**Models, Biological**: Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.

#### Example 55.13 Exponential and Weibull Survival Analysis :: SAS/STAT(R) 12.1 User's Guide

... you compare two models that have the same**likelihood**

**function**. If you do not have identical

**likelihood**

**functions**, using DIC for ... There are two ways to program the log-

**likelihood**

**function**in PROC MCMC. You can use the SAS

**functions**LOGPDF and LOGSDF. ... Using the GENERAL

**function**, you can obtain identical posterior samples with two log-

**likelihood**

**functions**that differ only by a ... The two assignment statements that are commented out calculate the log-

**likelihood**

**function**by using the SAS

**functions**LOGPDF ...

support.sas.com/documentation/cdl/en/statug/65328/HTML/default/statug_mcmc_examples19.htm

#### Microbial community dynamics in a seasonally anoxic fjord: Saanich Inlet, British Columbia - Zaikova - 2009 - Environmental...

S2. Maximum**likelihood**tree of α-, β- and γ-proteobacterial of partial SSU rDNA sequences (~1400 bp region) recovered from the ... The axis explaining the most variance was used to generate correlation plots for O2 in B and D. As a

**function**of depth and O2 ... S3. Maximum

**likelihood**tree of partial bacterial SSU rDNA (excluding α-, β- and γ-proteobacterial) sequences (~1400 bp region) ... Bootstrap values (%) are based on 100 replicates using the maximum

**likelihood**method and are shown for branches with greater ...

onlinelibrary.wiley.com/doi/10.1111/j.1462-2920.2009.02058.x/abstract

#### Lesson 4.2 **Likelihood** **function** and maximum **likelihood** - University of California, Santa Cruz | Coursera

The **likelihood**

**function**is this density

**function**. thought of as a

**function**of theta.. So we can write this L of theta given y. ... Lesson 4.2

**Likelihood**

**function**and maximum

**likelihood**. To view this video please enable JavaScript, and consider upgrading to a ... It looks like the same

**function**, but up here this is a

**function**of y given theta.. And now we're thinking of it as a

**function**... We can now think about this expression as a

**function**of theta.. This is a concept of a

**likelihood**.. ...

https://tr.coursera.org/learn/bayesian-statistics/lecture/9dWnA/lesson-4-2-likelihood-function-and-maximum-likelihood

#### Prediction of patient-specific risk for fetal loss using maternal characteristics and first- and second-trimester maternal...

**Likelihood**

**Functions**. Maternal Age. Parity. Pregnancy. Pregnancy Outcome*. Pregnancy Trimester, First. Pregnancy Trimester, ... Separate

**likelihood**ratios were estimated for significant maternal characteristics and serum markers. Patient-specific risk was ... calculated by multiplying the incidence of fetal loss by the

**likelihood**ratios for each maternal characteristic and for ...

biomedsearch.com/nih/Prediction-patient-specific-risk-fetal/18771987.html

#### FISH shows that Desulfotomaculum spp. are the dominating sulfate-reducing bacteria in a pristine aquifer.

**Likelihood**

**Functions**. Models, Genetic. Oligonucleotides. Phylogeny*. Sequence Analysis, DNA. Water Microbiology*. Chemical. ...

biomedsearch.com/nih/FISH-shows-that-Desulfotomaculum-spp/15085304.html

#### Procalcitonin in detecting neonatal nosocomial sepsis.

**Likelihood**

**Functions**. Protein Precursors / blood*. ROC Curve. Sensitivity and Specificity. Sepsis / diagnosis*. ...

biomedsearch.com/nih/Procalcitonin-in-detecting-neonatal-nosocomial/22933097.html

#### Testing consumer perception of nutrient content claims using conjoint analysis.

**Likelihood**

**Functions**. Logistic Models. Male. Middle Aged. Nutrition Policy*. Perception. Sex Distribution. United States. ... Utility

**functions**were constructed using conjoint analysis, based on multiple logistic regression and maximum

**likelihood**...

biomedsearch.com/nih/Testing-consumer-perception-nutrient-content/20074390.html

#### Accuracy of electronically reported 'meaningful use' clinical quality measures: a cross-sectional study.

**Likelihood**

**Functions**. Male. Meaningful Use*. Middle Aged. Predictive Value of Tests. Sensitivity and Specificity. ... Positive

**likelihood**ratios ranged from 2.34 to 24.25 and negative

**likelihood**ratios from 0.02 to 0.61. Differences between ... Sensitivity, specificity, positive and negative predictive values, positive and negative

**likelihood**ratios, and absolute rates ...

biomedsearch.com/nih/Accuracy-Electronically-Reported-Meaningful-Use/23318309.html

#### Phylogenomics and a posteriori data partitioning resolve the Cretaceous angiosperm radiation Malpighiales.

**Likelihood**

**Functions**. Malpighiaceae / classification, genetics*. Molecular Sequence Data. Phylogeny*. Species Specificity. ... 19045754 - Linearly filtered estimation of the time-domain green's

**function**from measurements of a.... 17271834 - Multichannel ... We found that commonly used a priori approaches for partitioning concatenated data in maximum

**likelihood**analyses, by gene or ...

biomedsearch.com/nih/Phylogenomics-posteriori-data-partitioning-resolve/23045684.html

#### Patent US6245517 - Ratio-based decisions and the quantitative analysis of cDNA micro-array images - Google Patents

In particular, the probability density of the ratio and the maximum-**likelihood**estimator for the distribution are derived, and ... 7 we can obtain a maximum-

**likelihood**estimator for c. The

**likelihood**

**function**is L. . (. c. ). =. ∏. i. =. 1. n. . (. 1. +. t ... the probability distribution

**function**for T is F. T. . (. t. ). =. . P. . (. X. ≤. t. . . Y. ,. Y. ,. 0. ). +. P. . (. X ... For X and Y independent, differentiation yields the probability density

**function**for T as f. T. . (. t. ). =. . ∫. 0. ∞. . y ...

google.com/patents/US6245517?dq=5,825,242

#### Statistical Analysis of Epidemiologic Data : Steve Selvin : 9780195172805

Maximum**likelihood**estimation and

**likelihood**

**functions**; F. Problemsshow more ... intuitive arguments that exponential survival times cause the hazard

**function**to be constant. He added a dozen new applied ...

https://bookdepository.com/Statistical-Analysis-Epidemiologic-Data-Steve-Selvin/9780195172805

#### Influence of sex, age, body mass index, and smoking on alcohol intake and mortality | The BMJ

which is (proportional to) the**likelihood**

**function**for the Poisson distribution. Thus, data can be analysed in a model where ( ... Letting d be the number of people with di=1, we get the

**likelihood**function29 ... If the underreporting was equal at all levels the observed risk

**function**would be moved to the left of the true risk

**function**, ... The risk

**function**of alcohol and mortality was U shaped (fig 1). There was no interaction between either sex or age and the ...

bmj.com/content/308/6924/302.long

#### Two Models of Measurements and the Investment Accelerator

These two models of the reporting agency imply different**likelihood**

**functions**. A model of the investment accelerator is used as ... These two models of the reporting agency imply different

**likelihood**

**functions**. A model of the investment accelerator is used as ... File

**Function**: full text. Download Restriction: Access to full text is restricted to subscribers. See http://www.journals. ...

https://ideas.repec.org/a/ucp/jpolec/v97y1989i2p251-87.html

#### Deriving a neutral model of species abundance from fundamental mechanisms of population dynamics - HE - 2005 - Functional...

Given observed abundances of s species n = {n1, n2, … , ns}, the log-**likelihood**

**function**of model 5 is . The maximum

**likelihood**... The maximum

**likelihood**

**function**of model 5 is easy to compute. ... were obtained by maximizing this log-

**likelihood**

**function**. The ...

**function**xi (see Zillinger 2003, p. 36); many mathematical programs, such as Maple or Mathematica, have standard built-in ... 2003). It is easy to show from equation 1 that at steady state (i.e. t → ∞) equation 2 is a

**function**of the birth and death ...

onlinelibrary.wiley.com/doi/10.1111/j.0269-8463.2005.00944.x/full

#### Log-normal distribution - Wikipedia

... we can write the log-**likelihood**

**function**thus:. ℓ. (. μ. ,. σ. ∣. x. 1. ,. x. 2. ,. …. ,. x. n. ). =. −. ∑. k. ln. . x. k. +. ... Cumulative distribution

**function**[edit]. The cumulative distribution

**function**is. F. X. (. x. ). =. Φ. (. (. ln. . x. ). −. μ. ... where erfc is the complementary error

**function**.. Characteristic

**function**and moment generating

**function**[edit]. All moments of ... Since the first term is constant with regard to μ and σ, both logarithmic

**likelihood**

**functions**, ℓ. L. {\displaystyle \ell _{L}} ...

https://en.wikipedia.org/wiki/Log_normal

#### PPT - Statistical Methods for Quantitative Trait Loci (QTL) Mapping II PowerPoint Presentation - ID:3863933

**Likelihood**

**function**+ model complexity (eg # QTLs) *Cross validation test *Sequential permutation tests ... to implement the srm inductive principle in learning algorithms one has to minimize the risk in a given set of

**functions**by ...

slideserve.com/quasar/statistical-methods-for-quantitative-trait-loci-qtl-mapping-ii

#### A capture-recapture model for exploring multi-species synchrony in survival - Lahoz-Monfort - 2010 - Methods in Ecology and...

... the overall**likelihood**

**function**for all species together can be written as the product of the individual

**likelihoods**. This is ... The relationship with covariates is handled through fS(.), a species-specific

**function**of ns covariates cSi. The

**function**could ...

**likelihood**

**functions**can be constructed individually for each of the species involved in the model. Following standard notation ... Survival ΦSP(Y) for species S in site P would be related to a species-and-site-specific

**function**fSP(.) of a set of nSP ...

onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2010.00050.x/full

#### The ABCs of Experimental Evolution

It avoids the direct evaluation of a**likelihood**

**function**, which is computationally expensive. We first describe the ... P. Marjoram, J. Molitor, V. Plagnol, and S. Tavaré, "Markov chain Monte Carlo without

**likelihoods**," Proceedings of the National ... Observed data from experimental evolution studies can be assumed to be

**function**of mutant sample sizes, selection coefficients ...

https://hindawi.com/journals/isrn/2013/467943/

#### Fitting a Mixture of Exponential Distributions for Patient's Length of Stay

It was described by as a result of the invariance of the mixture**likelihood**

**function**under the relabeling of the mixture ... The IF-ELSE statements enable different values of LOS to have different log-

**likelihood**

**functions**, depending on whether the ... Using Bayes' theorem, the

**likelihood**

**function**and prior distributions determine the posterior distribution of , and as follows ... is the density

**function**for the inverse-gamma distribution. Priors of this type are often called diffuse priors . The prior ...

support.sas.com/rnd/app/stat/examples/BayesMixtureExp/new_example/

#### Statistical Model Analysis - Wolfram Mathematica 8 Documentation

For a number of common statistical models, this is accomplished in Mathematica by way of fitting**functions**that construct ... Available model fitting

**functions**fit linear, generalized linear, and nonlinear models. ... such that the log of the quasi-

**likelihood**

**function**for the data point is given by . The variance

**function**for a model can be ... is the fitted

**function**and gives the fitted

**function**as a pure

**function**. gives the list of

**functions**, with being the constant ...

reference.wolfram.com/legacy/v8/tutorial/StatisticalModelAnalysis.html

#### Plus it

... is modeled by a**likelihood**

**function**p(e,r): the probability of sensorimotor signals (e) given actual rotation of the eye (r) ( ... The perceived target location is represented by a density

**function**px(x,vk,hk−1,ek−1,mk−1), which makes explicit the dependence ... Examples of

**likelihood's**and posteriors are shown for eye positions −40° (1) and 20° (2) away from forward view. Difference in ... Mean change in MGA averaged across all subjects as a

**function**of fixation eccentricity (angle between fixation and target). ...

jn.physiology.org/content/97/6/4203

#### Conjugate priors and variable selection for Bayesian quantile regression

These challenges are rarely addressed via either penalized**likelihood**

**function**or stochastic search variable selection. These ... These challenges are rarely addressed via either penalized

**likelihood**

**function**or stochastic search variable selection. These ... "A sandwich

**likelihood**correction for Bayesian quantile regression based on the misspecified asymmetric Laplace density," ... "Quasi-maximum

**likelihood**estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, ...

https://ideas.repec.org/a/eee/csdana/v64y2013icp209-219.html

#### Footbal ordinal model: examination and predictions | R-bloggers

link**function**and

**likelihood**. Looking at the vignette 'Analysis of ordinal data with cumulative link models - estimation with ... Slice can be used to plot the behavior of the

**likelihood**as

**function**of parameter estimates. It does give one plot for each ... This means three sections; A look at

**likelihood**and link

**function**, a model interpretation part, which focuses on the effect of ... fbpredict.polr <-

**function**(object,club1,club2) {. top <- data.frame(OffenseClub=c(club1,club2),DefenseClub=c(club2,club1), ...

https://r-bloggers.com/footbal-ordinal-model-examination-and-predictions/

#### IEEE Xplore: IEEE Transactions on Image Processing - ( Volume 18 ...

Examples of such**likelihood**

**functions**include the Poisson distribution and an exponential dispersion (ED) model that can ... We present in this paper a different formulation where the implicit

**function**is modeled as a continuous parametric

**function**... In this paper, we propose methods for system calibration and 3-D scene reconstruction by maximum

**likelihood**estimati... View ... is the sum of a Gaussian distributed electronic noise component and another random variable whose log-

**likelihood**

**function**...

ieeexplore.ieee.org/xpl/tocresult.jsp?reload=true&isnumber=4919437&punumber=83

#### Star Trek (2009) / Headscratchers - TV Tropes

Bars look and**function**in the same way, San Francisco Bay hasn't changed at all (take THAT, global warming!), the flag of the ... The statistical

**likelihood**that our plan will succeed is less than 4.3%. [interrupted by Kirk] In the event that I do not ... But, it never

**functions**the same. Through the course of the movie, it's shown to be capable of sending ships through time ( ... It must be -wait for it-Sabotage! Seriously, I could walk into a bar in 1809 and it'd still

**function**pretty much the same way, ...

tvtropes.org/pmwiki/pmwiki.php/Headscratchers/StarTrek2009

**Decoding methods**: In coding theory, decoding is the process of translating received messages into codewords of a given code. There have been many common methods of mapping messages to codewords.

**Hyperparameter**: In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis.

**Clonal Selection Algorithm**: In artificial immune systems, Clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation.

**Interval boundary element method**: Interval boundary element method is classical boundary element method with the interval parameters.

**Inverse probability weighting**: Inverse probability weighting is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.

**Vladimir Andreevich Markov**: Vladimir Andreevich Markov (; May 8, 1871 – January 18, 1897) was a Russian mathematician, known for proving the Markov brothers' inequality with his older brother Andrey Markov. He died of tuberculosis at the age of 25.

**Monte Carlo methods for option pricing**: In mathematical finance, a Monte Carlo option model uses Monte Carlo methods Although the term 'Monte Carlo method' was coined by Stanislaw Ulam in the 1940s, some trace such methods to the 18th century French naturalist Buffon, and a question he asked about the results of dropping a needle randomly on a striped floor or table. See Buffon's needle.

**Negative probability**: The probability of the outcome of an experiment is never negative, but quasiprobability distributions can be defined that allow a negative probability for some events. These distributions may apply to unobservable events or conditional probabilities.

**Matrix model**: == Mathematics and physics ==

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**An evaluation of elongation factor 1 alpha as a phylogenetic marker for eukaryotes.**

Elongation factor 1 alpha (EF-1 alpha) is a highly conserved ubiquitous protein involved in translation that has been suggested to have desirable properties for phylogenetic inference. To examine the utility of EF-1 alpha as a phylogenetic marker for eukaryotes, we studied three properties of EF-1 alpha trees: congruency with other phyogenetic markers, the impact of species sampling, and the degree of substitutional saturation occurring between taxa. Our analyses indicate that the EF-1 alpha tree is congruent with some other molecular phylogenies in identifying both the deepest branches and some recent relationships in the eukaryotic line of descent. However, the topology of the intermediate portion of the EF-1 alpha tree, occupied by most of the protist lineages, differs for different phylogenetic methods, and bootstrap values for branches are low. Most problematic in this region is the failure of all phylogenetic methods to resolve the monophyly of two higher-order protistan taxa, the Ciliophora and the Alveolata. JACKMONO analyses indicated that the impact of species sampling on bootstrap support for most internal nodes of the eukaryotic EF-1 alpha tree is extreme. Furthermore, a comparison of observed versus inferred numbers of substitutions indicates that multiple overlapping substitutions have occurred, especially on the branch separating the Eukaryota from the Archaebacteria, suggesting that the rooting of the eukaryotic tree on the diplomonad lineage should be treated with caution. Overall, these results suggest that the phylogenies obtained from EF-1 alpha are congruent with other molecular phylogenies in recovering the monophyly of groups such as the Metazoa, Fungi, Magnoliophyta, and Euglenozoa. However, the interrelationships between these and other protist lineages are not well resolved. This lack of resolution may result from the combined effects of poor taxonomic sampling, relatively few informative positions, large numbers of overlapping substitutions that obscure phylogenetic signal, and lineage-specific rate increases in the EF-1 alpha data set. It is also consistent with the nearly simultaneous diversification of major eukaryotic lineages implied by the "big-bang" hypothesis of eukaryote evolution. (+info)

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**Unusually high evolutionary rate of the elongation factor 1 alpha genes from the Ciliophora and its impact on the phylogeny of eukaryotes.**

The elongation factor 1 alpha (EF-1 alpha) has become widely employed as a phylogenetic marker for studying eukaryotic evolution. However, a disturbing problem, the artifactual polyphyly of ciliates, is always observed. It has been suggested that the addition of new sequences will help to circumvent this problem. Thus, we have determined 15 new ciliate EF-1 alpha sequences, providing for a more comprehensive taxonomic sampling of this phylum. These sequences have been analyzed together with a representation of eukaryotic sequences using distance-, parsimony-, and likelihood-based phylogenetic methods. Such analyses again failed to recover the monophyly of Ciliophora. A study of the substitution rate showed that ciliate EF-1 alpha genes exhibit a high evolutionary rate, produced in part by an increased number of variable positions. This acceleration could be related to alterations of the accessory functions acquired by this protein, likely to those involving interactions with the cytoskeleton, which is very modified in the Ciliophora. The high evolutionary rate of these sequences leads to an artificial basal emergence of some ciliates in the eukaryotic tree by effecting a long-branch attraction artifact that produces an asymmetric topology for the basal region of the tree. The use of a maximum-likelihood phylogenetic method (which is less sensitive to long-branch attraction) and the addition of sequences to break long branches allow retrieval of more symmetric topologies, which suggests that the asymmetric part of the tree is most likely artifactual. Therefore, the sole reliable part of the tree appears to correspond to the apical symmetric region. These kinds of observations suggest that the general eukaryotic evolution might have consisted of a massive radiation followed by an increase in the evolutionary rates of certain groups that emerge artificially as early branches in the asymmetric base of the tree. Ciliates in the case of the EF-1 alpha genes would offer clear evidence for this hypothesis. (+info)

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**Interaction of process partitions in phylogenetic analysis: an example from the swallowtail butterfly genus Papilio.**

In this study, we explored how the concept of the process partition may be applied to phylogenetic analysis. Sequence data were gathered from 23 species and subspecies of the swallowtail butterfly genus Papilio, as well as from two outgroup species from the genera Eurytides and Pachliopta. Sequence data consisted of 1,010 bp of the nuclear protein-coding gene elongation factor-1 alpha (EF-1 alpha) as well as the entire sequences (a total of 2,211 bp) of the mitochondrial protein-coding genes cytochrome oxidase I and cytochrome oxidase II (COI and COII). In order to examine the interaction between the nuclear and mitochondrial partitions in a combined analysis, we used a method of visualizing branch support as a function of partition weight ratios. We demonstrated how this method may be used to diagnose error at different levels of a tree in a combined maximum-parsimony analysis. Further, we assessed patterns of evolution within and between subsets of the data by implementing a multipartition maximum-likelihood model to estimate evolutionary parameters for various putative process partitions. COI third positions have an estimated average substitution rate more than 15 times that of EF-1 alpha, while COII third positions have an estimated average substitution rate more than 22 times that of EF-1 alpha. Ultimately, we found that although the mitochondrial and nuclear data were not significantly incongruent, homoplasy in the fast-evolving mitochondrial data confounded the resolution of basal relationships in the combined unweighted parsimony analysis despite the fact that there was relatively strong support for the relationships in the nuclear data. We conclude that there may be shortcomings to the methods of "total evidence" and "conditional combination" because they may fail to detect or accommodate the type of confounding bias we found in our data. (+info)

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**Diagnosing anaemia in pregnancy in rural clinics: assessing the potential of the Haemoglobin Colour Scale.**

Anaemia in pregnancy is a common and severe problem in many developing countries. Because of lack of resources and staff motivation, screening for anaemia is often solely by clinical examination of the conjunctiva or is not carried out at all. A new colour scale for the estimation of haemoglobin concentration has been developed by WHO. The present study compares the results obtained using the new colour scale on 729 women visiting rural antenatal clinics in Malawi with those obtained by HemoCue haemoglobinometer and electronic Coulter Counter and with the assessment of anaemia by clinical examination of the conjunctiva. Sensitivity using the colour scale was consistently better than for conjunctival inspection alone and interobserver agreement and agreement with Coulter Counter measurements was good. The Haemoglobin Colour Scale is simple to use, well accepted, cheap and gives immediate results. It shows considerable potential for use in screening for anaemia in antenatal clinics in settings where resources are limited. (+info)

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**Laboratory assay reproducibility of serum estrogens in umbilical cord blood samples.**

We evaluated the reproducibility of laboratory assays for umbilical cord blood estrogen levels and its implications on sample size estimation. Specifically, we examined correlation between duplicate measurements of the same blood samples and estimated the relative contribution of variability due to study subject and assay batch to the overall variation in measured hormone levels. Cord blood was collected from a total of 25 female babies (15 Caucasian and 10 Chinese-American) from full-term deliveries at two study sites between March and December 1997. Two serum aliquots per blood sample were assayed, either at the same time or 4 months apart, for estrone, total estradiol, weakly bound estradiol, and sex hormone-binding globulin (SHBG). Correlation coefficients (Pearson's r) between duplicate measurements were calculated. We also estimated the components of variance for each hormone or protein associated with variation among subjects and variation between assay batches. Pearson's correlation coefficients were >0.90 for all of the compounds except for total estradiol when all of the subjects were included. The intraclass correlation coefficient, defined as a proportion of the total variance due to between-subject variation, for estrone, total estradiol, weakly bound estradiol, and SHBG were 92, 80, 85, and 97%, respectively. The magnitude of measurement error found in this study would increase the sample size required for detecting a difference between two populations for total estradiol and SHBG by 25 and 3%, respectively. (+info)

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**Maximum-likelihood generalized heritability estimate for blood pressure in Nigerian families.**

Elevated blood pressure (BP) is more common in relatives of hypertensives than in relatives of normotensives, indicating familial resemblance of the BP phenotypes. Most published studies have been conducted in westernized societies. To assess the ability to generalize these estimates, we examined familial patterns of BP in a population-based sample of 510 nuclear families, including 1552 individuals (320 fathers, 370 mothers, 475 sons, and 387 daughters) from Ibadan, Nigeria. The prevalence of obesity in this community is low (body mass index: fathers, 21.6; mothers, 23.6; sons, 19.2; and daughters=21.0 kg/m2). The BP phenotype used in all analyses was created from the best regression model by standardizing the age-adjusted systolic blood pressure (SBP) and diastolic blood pressure (DBP) to 0 mean and unit variance. Heritability was estimated by use of the computer program SEGPATH from the most parsimonious model of "no spouse and neither gender nor generation difference" as 45% for SBP and 43% for DBP. The lack of a significant spouse correlation is consistent with little or no influence of the common familial environment. However, the heritability estimate of <50% for both SBP and DBPs reinforces the importance of the nonshared environmental effect. (+info)

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**A gene for X-linked idiopathic congenital nystagmus (NYS1) maps to chromosome Xp11.4-p11.3.**

Congenital nystagmus (CN) is a common oculomotor disorder (frequency of 1/1,500 live births) characterized by bilateral uncontrollable ocular oscillations, with onset typically at birth or within the first few months of life. This condition is regarded as idiopathic, after exclusion of nervous and ocular diseases. X-linked, autosomal dominant, and autosomal recessive modes of inheritance have been reported, but X-linked inheritance is probably the most common. In this article, we report the mapping of a gene for X-linked dominant CN (NYS1) to the short arm of chromosome X, by showing close linkage of NYS1 to polymorphic markers on chromosome Xp11.4-p11.3 (maximum LOD score of 3.20, over locus DXS993). Because no candidate gene, by virtue of its function, has been found in this region of chromosome Xp, further studies are required, to reduce the genetic interval encompassing the NYS1 gene. It is hoped that the complete gene characterization will address the complex pathophysiology of CN. (+info)

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**A note on power approximations for the transmission/disequilibrium test.**

The transmission/disequilibrium test (TDT) is a popular method for detection of the genetic basis of a disease. Investigators planning such studies require computation of sample size and power, allowing for a general genetic model. Here, a rigorous method is presented for obtaining the power approximations of the TDT for samples consisting of families with either a single affected child or affected sib pairs. Power calculations based on simulation show that these approximations are quite precise. By this method, it is also shown that a previously published power approximation of the TDT is erroneous. (+info)

#### ratios

- Separate likelihood ratios were estimated for significant maternal characteristics and serum markers. (biomedsearch.com)
- Patient-specific risk was calculated by multiplying the incidence of fetal loss by the likelihood ratios for each maternal characteristic and for different serum marker combinations. (biomedsearch.com)
- Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and absolute rates of recommended care were measured. (biomedsearch.com)
- Positive likelihood ratios ranged from 2.34 to 24.25 and negative likelihood ratios from 0.02 to 0.61. (biomedsearch.com)

#### regression

- Utility functions were constructed using conjoint analysis, based on multiple logistic regression and maximum likelihood estimation. (biomedsearch.com)

#### probability function

- Note that this formulation of the exponential distribution is different from what is used in the SAS probability function PDF. (sas.com)

#### exponential

- Throughout the text he enriched existing discussions with new elements, including the analysis of multi-level categorical data and simple, intuitive arguments that exponential survival times cause the hazard function to be constant. (bookdepository.com)
- The two assignment statements that are commented out calculate the log-likelihood function by using the SAS functions LOGPDF and LOGSDF for the exponential distribution. (sas.com)
- This relationship is true regardless of the base of the logarithmic or exponential function. (wikipedia.org)

#### maximum

- Lesson 4 takes the frequentist view, demonstrating maximum likelihood estimation and confidence intervals for binomial data. (coursera.org)
- We found that commonly used a priori approaches for partitioning concatenated data in maximum likelihood analyses, by gene or by codon position, performed poorly relative to the use of partitions identified a posteriori using a Bayesian mixture model. (biomedsearch.com)
- In particular, the probability density of the ratio and the maximum-likelihood estimator for the distribution are derived, and an iterative procedure for signal calibration is developed. (google.com)

#### density function

- is the density function for the inverse-gamma distribution. (sas.com)

#### Models

- These two models of the reporting agency imply different likelihood functions. (repec.org)
- For a number of common statistical models, this is accomplished in Mathematica by way of fitting functions that construct FittedModel objects. (wolfram.com)
- Available model fitting functions fit linear, generalized linear, and nonlinear models. (wolfram.com)
- Models of this type can be fitted using the LinearModelFit function. (wolfram.com)

#### Model

- The major difference between model fitting functions such as LinearModelFit and functions such as Fit and FindFit is the ability to easily obtain diagnostic information from the FittedModel objects. (wolfram.com)

#### data

- Beginning with a binomial likelihood and prior probabilities for simple hypotheses, you will learn how to use Bayes' theorem to update the prior with data to obtain posterior probabilities. (coursera.org)

#### distribution

- PROC MCMC obtains samples from the desired posterior distribution, which is determined by the prior and likelihood specified. (sas.com)