Web 2.0, Library 2.0, and the Hyperlinked Library

Abstract

Discussions surrounding the concepts of Web 2.0/Library 2.0 are increasing among the library community. This column outlines key principles behind Web 2.0 and provides a brief explanation of social tools, such as blogs, RSS feeds, podcasting, and wikis. The author also provides specific uses and applications of these tools within the library environment to illustrate the Library 2.0 concept. An open framework for library communication or hyperlinked library can result if Library 2.0 philosophies are fully utilized.     

Coming to TERMS with Electronic Resource Management: An Interview with Jill Emery,Graham Stone,and Peter McCracken

ABSTRACT

In this column of Serial Conversations, Elan May Rinck interviews Jill Emery, Graham Stone, and Peter McCracken, the editors of TERMS (Techniques in E-Resource Management) 2.0. Emery, Stone, and McCracken discuss developing best practices for electronic resource management and what lies ahead for TERMS 2.0.     

Social Bookmarking–Repository–Networking: Possibilities for Support of Teaching and Learning in Higher Education

Abstract

Social bookmarking, repositories, networking, and other Web 2.0 technologies have the capacity to engage enormous numbers of ordinary people in networking, sharing, and working with digital resources. The Faculty of Education at The University of Hong Kong has been pioneering new educational applications. Through grants from the University we have developed a social bookmarking and repository system based on Web 2.0 ideas. RISAL—Repository of Interactive Social Assets for Learning has an unrealized potential to support teaching and learning at the University level. It is designed to facilitate and encourage sharing and collaboration of teachers and students with similar interests or learning foci across levels and programs. This form of learning support builds on and encourages the Web 2.0 culture of social networking and contribution of the individual to the community. In addition, the system is designed to incorporate compliance with Hong Kong's Copyright Ordinance (Creative Commons). The authors discuss the RISAL and describe its features and benefits that this system can offer to teaching and learning.     

Comparing the BLUEs Under Two Linear Models

In this article, we consider two linear models, ?₁ = {y, X β, V ₁} and ?₂ = {y, X β, V ₂}, which differ only in their covariance matrices. Our main focus lies on the difference of the best linear unbiased estimators, BLUEs, of X β under these models. The corresponding problems between the models {y, X β, I _n } and {y, X β, V}, i.e., between the OLSE (ordinary least squares estimator) and BLUE, are pretty well studied. Our purpose is to review the corresponding considerations between the BLUEs of X β under ?₁ and ?₂. This article is an expository one presenting also new results.     

A study of generalized skew-normal distribution

Following the paper by Genton and Loperfido [Generalized skew-elliptical distributions and their quadratic forms, Ann. Inst. Statist. Math. 57 (2005), pp. 389–401], we say that Z has a generalized skew-normal distribution, if its probability density function (p.d.f.) is given by f(z)=2φ _p (z; ξ, Ω)π (z?ξ), z∈? ^p , where φ _p (·; ξ, Ω) is the p-dimensional normal p.d.f. with location vector ξ and scale matrix Ω, ξ∈? ^p , Ω>0, and π is a skewing function from ? ^p to ?, that is 0≤π (z)≤1 and π (?z)=1?π (z), ? z∈? ^p . First the distribution of linear transformations of Z are studied, and some moments of Z and its quadratic forms are derived. Next we obtain the joint moment-generating functions (m.g.f.’s) of linear and quadratic forms of Z and then investigate conditions for their independence. Finally explicit forms for the above distributions, m.g.f.’s and moments are derived when π (z)=κ (α′z), where α∈? ^p and κ is the normal, Laplace, logistic or uniform distribution function.     

Improved ridge estimators in a linear regression model

In this paper, the notion of the improved ridge estimator (IRE) is put forward in the linear regression model y=X β+e. The problem arises if augmenting the equation 0=c′α+ε instead of 0=C α+? to the model. Three special IREs are considered and studied under the mean-squared error criterion and the prediction error sum of squares criterion. The simulations demonstrate that the proposed estimators are effective and recommendable, especially when multicollinearity is severe.     

Three-term asymptotic expansion: A semi-Markovian random walk with a generalized beta distributed interference of chance

A semi-Markovian random walk process (X(t)) with a generalized beta distribution of chance is considered. The asymptotic expansions for the first four moments of the ergodic distribution of the process are obtained as E(ζ_n) → ∞ when the random variable ζ_n has a generalized beta distribution with parameters (s, S, α, β); ?α, β > 1,?0? ? s < S < ∞. Finally, the accuracy of the asymptotic expansions is examined by using the Monte Carlo simulation method.     

Two graphical displays for the detection of potentially influential subsets in regression

In the context of the general linear model Y=Xβ+ε, the matrix P_z =Z(Z^TZ)^?1 Z^T , where Z=(X: Y), plays an important role in determining least squares results. In this article we propose two graphical displays for the off-diagonal as well as the diagonal elements of P_Z . The two graphs are based on simple ideas and are useful in the detection of potentially influential subsets of observations in regression. Since P_Z is invariant with respect to permutations of the columns of Z, an added advantage of these graphs is that they can be used to detect outliers in multivariate data where the rows of Z are usually regarded as a random sample from a multivariate population. We also suggest two calibration points, one for the diagonal elements of P_Z and the other for the off-diagonal elements. The advantage of these calibration points is that they take into consideration the variability of the off-diagonal as well as the diagonal elements of P_Z . They also do not suffer from masking.     

DECOMPOSITIONS OF THE QUEUE LENGTH DISTRIBUTIONS IN THE MAP/G/1 QUEUE UNDER MULTIPLE AND SINGLE VACATIONS WITH N-POLICY

In this paper, we establish an explicit form of matrix decompositions for the queue length distributions of the MAP/G/1 queues under multiple and single vacations with N-policy. We show that the vector generating function Y (z) of the queue length at an arbitrary time and X (z) at departures are decomposed into Y (z) = p _idle (z)ζ _Y (z) and X (z) = p _idle (z)ζ _X (z) where p _idle (z) is the vector generating function of the queue length at an arbitrary epoch at which the server is not in service, and ζ _Y (z) and ζ _X (z) are unidentified matrix generating functions.     

Dependence Structure of a Class of Symmetric Distributions

Abstract

In this article, dependence structure of a class of symmetric distributions is considered. Let X and Y be two n-dimensional random vectors having such distributions. We investigate conditions on the generators of densities of X and Y such that X is MTP₂, and X and Y can be compared in the multivariate likelihood ratio order. Nonnegativity of the covariance between functions of two adjacent order statistics of X is also given.     

On the BLUEs in Two Linear Models via C. R. Rao's Pandora's Box

We consider the estimation of the parameters in two partitioned linear models, denoted by 𝒜 = {y, X ₁ β ₁ + X ₂ β ₂, V _𝒜} and ? = {y, X ₁ β ₁ + X ₂ β ₂, V _?}, which we call full models. Correspondingly, we define submodels 𝒜₁ = {y, X ₁ β ₁, V _𝒜} and ?₁ = {y, X ₁ β ₁, V _?}. Using the so-called Pandora's Box approach introduced by Rao (1971 Rao , C. R. ( 1971 ). Unified theory of linear estimation . Sankhy?, Ser. A 33 : 371 – 394 . [Corrigendum (1972), 34, p. 194, 477.]  [Google Scholar], we give new necessary and sufficient conditions for the equality between the best linear unbiased estimators (BLUEs) of X ₁ β ₁ under 𝒜₁ and ?₁ as well as under 𝒜 and ?. In our considerations we will utilise the Frisch–Waugh–Lovell theorem which provides a connection between the full model 𝒜 and the reduced model 𝒜 _r  = {M ₂ y, M ₂ X ₁ β ₁, M ₂ V _𝒜 M ₂} with M ₂ being an appropriate orthogonal projector. Moreover, we consider the equality of the BLUEs under the full models assuming that they are equal under the submodels.     

Questions & Answers: The Role of Mathematical Models in Clinical Research

Given any generalized inverse (X'X)^? appropriate to normal equations X'Xb ⁰ = X'y for the linear model y = Xb + e, a procedure is given for obtaining from it a generalized inverse appropriate to a restricted model having restrictions P'b = 0 for P'b nonestimable.     

Best Quadratic Unbiased Prediction in a General Linear Model with Stochastic Regression Coefficients

In this article, we discuss on how to predict a combined quadratic parametric function of the form β′ H β + hσ² in a general linear model with stochastic regression coefficients denoted by y  =  X β +  e . Firstly, the quadratic predictability of β′ H β + hσ² is investigated to obtain a quadratic unbiased predictor (QUP) via a general method of structuring an unbiased estimator. This QUP is also optimal in some situations and therefore we hope it will be a fine predictor. To show this idea, we apply the Lagrange multipliers method to this problem and finally reach the expected conclusion through permutation matrix techniques.     

Regression model with elliptically contoured errors

For the regression model y=X β+ε where the errors follow the elliptically contoured distribution, we consider the least squares, restricted least squares, preliminary test, Stein-type shrinkage and positive-rule shrinkage estimators for the regression parameters, β.

We compare the quadratic risks of the estimators to determine the relative dominance properties of the five estimators.     

Best quadratic predictions in finite populations

For a finite population and the resulting linear model Y=Xβ+e, the problem of the optimal invariant quadratic predictors including optimal invariant quadratic unbiased predictor and optimal invariant quadratic (potentially) biased predictor for the population quadratic quantities, f(H)=Y′HY , is of interest and has been previously considered in the literature for the case of HX=0. However, the special case does not contain all of situations at all. So, predicting f(H) in general situations may be of particular interest. In this paper, we make an effort to investigate how to offer a good predictor for f(H), not restricted yet to the mentioned case. Permutation matrix techniques play an important role in handling the process. The expected predictors are finally derived. In addition, we mention that the resulting predictors can be viewed as acceptable in all situations.     

A note on efficient simulation of multidimensional spatial autoregressive processes

In applications of spatial statistics, it is necessary to compute the product of some matrix W of spatial weights and a vector y of observations. The weighting matrix often needs to be adapted to the specific problems, such that the computation of Wy cannot necessarily be done with available R-packages. Hence, this article suggests one possibility treating such issues. The proposed technique avoids the computation of the matrix product by calculating each entry of Wy separately. Initially, a specific spatial autoregressive process is introduced. The performance of the proposed program is briefly compared to a basic program using the matrix multiplication.     

Prediction based on linear combinations of order statistics and bivariate concomitants in the case of multivariate elliptical distributions

In this paper, by considering a (3n+1) -dimensional random vector (X₀, X^T, Y^T, Z^T)^T having a multivariate elliptical distribution, we derive the exact joint distribution of (X₀, a^TX_(n), b^TY_[n], c^TZ_[n])^T, where a, b, c∈?ⁿ, X_(n)=(X₍₁₎, …, X_(n))^T, X₍₁₎<···<X_(n), is the vector of order statistics arising from X, and Y_[n]=(Y_[1], …, Y_[n])^T and Z_[n]=(Z_[1], …, Z_[n])^T denote the vectors of concomitants corresponding to X_(n) ((Y_[r], Z_[r])^T, for r=1, …, n, is the vector of bivariate concomitants corresponding to X_(r)). We then present an alternate approach for the derivation of the exact joint distribution of (X₀, X_(r), Y_[r], Z_[r])^T, for r=1, …, n. We show that these joint distributions can be expressed as mixtures of four-variate unified skew-elliptical distributions and these mixture forms facilitate the prediction of X_(r), say, based on the concomitants Y_[r] and Z_[r]. Finally, we illustrate the usefulness of our results by a real data.     

Reviews and Notices

Abstract

Longstaff's Studies in Statistics: Studies in Statistics. Social, Political, and Medical. By George Blundell Longstaff, M.A., M.B., etc. London: Edward Stanford. 1891. 8vo. Pp. 455. Maps and diagrams. Reviewed by S. W. Abbott.

Keynes on Statistics: The Scope and Method of Political Economy. By John Neville Keynes, M.A., London. Macmillan and Company. 1891. Pp. xiv, 359. Reviewed by Davis R. Dewey.

United States Census Bulletins: 1No. 39. March 16, 1891. Wealth and Resources of Alaska. By Ivan Petroff. Pp. 15.

United States Census Bulletins: No. 40. March 17. Population by Counties, North Central Division. Pp. 9.

United States Census Bulletins: No. 41. March 19. Agriculture, Truck Farming. By J. H. Hale. Pp. 12.

United States Census Bulletins: No. 42. March 20. Population by Counties, South Central and Western Divisions. Pp. 9.

United States Census Bulletins: No. 43. March 21. Coal Product West of the Mississippi River. By John H. Jones. Pp. 8.

United States Census Bulletins: No. 44. March 25. Distribution of Population in Accordance with Mean Relative Humidity of the Atmosphere. By Henry Gannett. Pp. 3.

United States Census Bulletins: No. 45. March 26. Granite. By William C. Day. Pp. 41.

United States Census Bulletins: No. 46. March 27. Railway Statistics of the New England States. By Henry C. Adams. Pp. 18.

United States Census Bulletins: No. 47. March 28. Distribution of Population by Drainage Basins. By Henry Gannett. Pp. 5.

United States Census Bulletins: No. 48. April 7. The White and Colored Population of the South. 1890. Pp. 27.

United States Census Bulletins: No. 49. April 14. Precious and Ornamental Stones and Diamond Cutting. By George Frederick Kunz. Pp. 8.

United States Census Bulletins: No. 50. April 15. Population of Rhode Island by Minor Civil Divisions. Pp. 3.

United States Census Bulletins: No. 51. April 16. Population of Vermont by Minor Civil Divisions. Pp. 4.

United States Census Bulletins: No. 52. April 17. Urban Population in 1890. Cities Containing 8000 Inhabitants or more. Pp. 9.

United States Census Bulletins: No. 53. April 20. Statistics of Education. Alaska, Arkansas, Delaware, Missouri, Iowa, Michigan, Minnesota, Mississippi, New Mexico, New York, North Dakota, Oregon, Texas, Utah, Washington, West Virginia, and forty-two Cities. By James H. Blodgett. Pp. 34.

United States Census Bulletins: No. 54. April 23. Public School Finances. By J. K. Upton. Pp. 12.

United States Census Bulletins: No. 55. April 24. The Relative Economy of Cable, Electric, and Animal Motive Power for Street Railways. By Charles H. Cooley. Pp. 17.

United States Census Bulletins: No. 56. April 25. Population of Maine by Minor Civil Divisions. Pp. 7.

United States Census Bulletins: No. 57. April 27. Population of Delaware by Minor Civil Divisions. Pp. 3.

United States Census Bulletins: No. 58. April 28. Population of Connecticut by Minor Civil Divisions. Pp. 3.

United States Census Bulletins: No. 59. April 29. Commercial Floriculture. By J. H. Hale. Pp. 11.

United States Census Bulletins: No. 60. April 30. Irrigation in New Mexico. By F. H. Newell. Pp. 14.

United States Census Bulletins: No. 61. May 8. The Production of Mica. By L. J. Childs. Pp. 6.

United States Census Bulletins: No. 62. May 9. Asylums for the Insane in the United States. By Dr. John S. Billings and W. H. Olcott. Pp. 32.

United States Census Bulletins: No. 63. May 11. Distribution of Population in Accordance with Latitude and Longitude. With diagrams. By Henry Gannett. Pp. 7.

United States Census Bulletins: No. 64. May 12. Foreign, National, State, and County Indebtedness. By J. K. Upton. Pp. 52.

United States Census Bulletins: No. 65. May 13. Distribution of Population in Accordance with Topographic Features. By Henry Gannett. Pp. 7.

United States Census Bulletins: No. 66. May 14. Floating Equipment on the Great Lakes. By Henry C. Adams. Pp. 11.

United States Census Bulletins: No. 68. May 16. Production of Manganese Ores. By Joseph D. Weeks. Pp. 5.

United States Census Bulletins: Mo. 69. May 18. Population of New Jersey by Minor Civil Divisions. Pp. 6.

United States Census Bulletins: No. 70. May 22. Statistics of Churches. By Henry K. Carroll. Pp. 27.

United States Census Bulletins: No. 71. May 23. Production of Bluestone. By William C. Day. Pp. 6.

Congress of Demography.

Old Age and Pauperism in England.

Mortality of English Clergymen.

Fire Statistics.