## S-VAR statistics

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## Protocols

## S-VAR specifications

### Information

Unique identifier | OMICS_25384 |
---|---|

Name | S-VAR |

Interface | Web user interface |

Restrictions to use | None |

Input data | An amino acid sequence. |

Input format | FASTA |

Computer skills | Basic |

Stability | Stable |

Maintained | Yes |

## S-VAR citations

(12)*library_books*

### Variable selection models for genomic selection using whole genome sequence data and singular value decomposition

[…] ese components. At this point, it is possible to omit some of the components with small singular values in S, which reflect noise on the estimates of X. The variance of the effects of the components i**s:Var**(s)=Var(V′b)=V′Vσb2=Iσb2.When applied to s, Henderson’s mixed model equations (MME) [] become:1N00S2+Iλbμ^s^=1′ySU′y,where S2=SU′US (since U′U=I), X′1=0 (a vector of zeros), since the genotypes a […]

*call_split*

### MicroRNA expression patterns in canine mammary cancer show significant differences between metastatic and non metastatic tumours

*call_split*See protocol

[…] formed using BRB-ArrayTools, Version 4.3.2 (developed by Dr. Richard Simon and BRB-Array Tools Development Team). For the unsupervised analysis, the variances of microRNAs were calculated using Excel’**s VAR**.S function. For the supervised analysis, Significance Analysis of Microarrays (SAM) test, which sets estimate of False Discovery Rate for multiple testing, was applied. Results of the analyses a […]

*call_split*

### The impact of rate heterogeneity on inference of phylogenetic models of trait evolution

[…] which assesses model ability to account for temporal variation (positive slopes show rate overestimations late in the phylogeny and underestimations early on), (v) against the variances of contrasts (**S.VAR**), signalling if models account for variation related to branch lengths (positive slopes show rate underestimation on long branches and overestimation on short ones), and (vi) against the weighte […]

*library_books*

### On the Apportionment of Population Structure

[…] is a discrete random variable taking the values of μW = E[W], μY = E[Y], μZ = E[Z] with corresponding probabilities α, β, γ respectively, and where C is some constant. Hence at the limit n→∞ we have,
**S=Var**(UXYZ)=α(μW−μ)2+β(μY−μ)2+γ(μZ−μ)2(8)
μ=αμW+βμY+γμZ
and S = 0 if and only if the three means are equal, i.e., μW = μY = μZ.Now consider three sequences of random variables Wi, Yi, Zi, i:1…n, inste […]

*library_books*

### Linear Increments with Non‐monotone Missing Data and Measurement Error

[…] We assume throughout Sections and that there is no measurement error, that is, e
t=0. Let
μ=E{(Y1⊤,…,YT⊤,X⊤)⊤} and
**Σ=Var**{(Y1⊤,…,YT⊤,X⊤)⊤}. Let μ
t denotes the subvector of μ corresponding to Y
t (t = 1,…,T + 1), where Y
T + 1 means X. Similarly, let Σ
s,t denotes the submatrix of Σ corresponding to (Y
s,Y
t) (1≤s, […]

*library_books*

### Fluctuations, Correlations and the Estimation of Concentrations inside Cells

[…] not fit within any of the two limits, knowing the behavior of the ACFs for both of them gives an indication of what may happen in between []. In these limits the ACFs are approximately of the form:
G(**s)=var**(N(s))∑iWi(s)Φi(s)(τ)(32)
with Φi(s)(0)=1, ∑iWi(s)=1 and Φi(s)(τ) characterized by a single correlation time, τi. Thus, the asymptotic correlation time, , studied in [, , ], is a weighted averag […]

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