Encyclopedia of virology/ 3, [Pom - Z] by Allan Granoff, Robert G. Webster

By Allan Granoff, Robert G. Webster

Lately, growth within the box of virology has complicated at an unparalleled cost. concerns reminiscent of AIDS have introduced the topic firmly into the general public area and its examine is not any longer constrained exclusively to professional groups.
The Encyclopedia of Virology is the biggest unmarried reference resource of present virological wisdom. it's also the 1st to compile all facets of the topic for a wide selection of readers. detailed in its use of concise 'mini-review' articles, the fabric covers organic, molecular, and clinical subject matters touching on viruses in animals, vegetation, micro organism, and insects.
More basic articles concentrate on the consequences of viruses at the immune procedure, the position of viruses in ailment, oncology, gene remedy, and evolution, plus a variety of comparable topics.
Drawing at the most modern learn, the editors have produced the definitive resource for either expert and common readers. Easy-to-use and meticulously prepared, the Encyclopedia of Virology clarifies and illuminates essentially the most complicated parts of latest learn. it is going to end up a useful addition to libraries, universities, clinical and nursing faculties, and learn associations round the world.
The moment version has been completely up to date with nearly forty new articles. This variation comprises extra illustrations and colour plates in each one quantity.

Key Features
* up to date completely with nearly forty new articles
* offers extra illustrations than the 1st version, with colour plates in every one volume
* includes a entire topic index in every one volume
* offers additional interpreting lists on the finish of every access, permitting quick access to the first literature
* broad cross-referencing method hyperlinks all comparable articles
* comprises the latest info of specific viruses defined on the seventh overseas Committee on Taxonomy and class of Viruses
* presents the power to go looking for entries alphabetically or through the taxonomical listings to entry articles of alternative viruses

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Extra info for Encyclopedia of virology/ 3, [Pom - Z]

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J. (1978). J. Amer. Statist. , 71, 328–338. 19. Singh, F. (1977). A Sequential Approach to Sample Surveys. Thesis, Meerut, India. D. SINGH SEQUENTIAL T 2 TEST The sequential T 2 test is an extension of the sequential t-test to multivariate data developed by Jackson and Bradley [2,3]. It is used to test the hypothesis (H0 ) that the expected value ξ of a p-variate multinormal distribution∗ equals a specified value ξ 0 , based on a random sample of size nX1 , X2 , . , Xn . The test is in the form of a sequential probability ratio test with likelihood ratio 2 /2 Nn (Tn ) = e−nλ ×1 F1 ( 12 n; 12 p; 12 nλ2 Tn2 (n−1+Tn2 )−1 ), where λ2 = (ξ 1 − ξ 0 ) −1 (ξ 1 − ξ 0 ) (with denoting the variance-covariance matrix of X) corresponds to the specified alternative hypothesis (H1 : ξ = ξ 1 ), ∞ 1 F1 (a; b; x) = j=0 with a [j] a[j] xi · , b[j] j = a(a + 1) · · · (a + j − 1), is the confluent hypergeometric function∗ , and Tn2 = n(X − ξ 0 ) S−1 (X − ξ 0 ), with X the sample arithmetic mean vector and S the sample variance–covariance matrix.

05 from Durbin and Watson [2, Part II, Table 4] for one independent regressor variable. 36. 36, we accept H0 at the 5% level. Durbin and Watson [2, Part II] show that the d-test is the locally most powerful invariant test∗ in the neighborhood of the null hypothesis. Using simulation, L’Esperance and Taylor [7] compare the d-test with tests based on BLUS∗ residuals and other statistics based on standardized residuals independent of the regressor matrix X: the d-test is found generally to be the most powerful test.

We now return to the finite sample. One measure of serial correlation is provided by the product-moment correlation between successive observations of the series: r(n) k,1 = (zi − zn )(zi−k − zn−k ) (zi − zn )2 (zi−k − zn−k )2 1/2 , (1) where the summations run from i = 1 + k to n and zn−j = (z1+k−j + · · · + zn−j )/(n − k), for j = 0 or k. Here, and elsewhere, we employ the superscript n to emphasise the length of the series with which we are dealing. For both computational and theoretical convenience, the means of the last n − k and first n − k observations, zn and zn−k , respectively, are usually replaced in (1) by the mean for the whole series, z = (z1 + · · · + zn )/n; we may also replace the corresponding separate variances (mean-square deviations about the mean) in the denominator of (1) by the variance for the whole series.

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