Asymptotic properties of a generalized regression-type predictor of a finite population variance in probability sampling

A system of predictors for estimating a finite population variance is defined and shown to be asymptotically design-unbiased (ADU) and asymptotically design-consistent (ADC) under probability sampling. An asymptotic mean squared error (MSE) of a generalized regression-type predictor, generated from the system, is obtained. The suggested predictor attains the minimum expected variance of any design-unbiased estimator when the superpopulation model is correct. The generalized regression-type predictor and the predictor suggested by Mukhopadhyay (1990) are compared.     

The social “R & D” paradigm in child and youth services: Building knowledge convivially

Adaptation of agricultural and industrial research and development models offers new methods for bridging the worlds of research and practice in child and youth services. Social R & D methodologies provide effective means for generating new social technologies because they are product oriented and use a wide array of traditional and nontraditional data sources. This article discusses one social R & D paradigm—Developmental Research and Utilization. Permanency planning and community support systems research are used to illustrate the past and potential application of this methodology to the field of child and youth services.     

Relationships of optimality for individual factors of a design

The optimality of two-factor experimental designs is studied in the dual senses of estimating contrasts in the parameters for each of the factors. The outline of comparison employed allows one to judge the performance of different designs for estimating contrasts of one set of parameters directly with the performance of the complementary set without going through a common intermediary step of considering all the parameters. The results hold for a wide class of optimality criteria (not merely D-, A- and E-optimality), which must satisfy a functional equation obtained in connection with our method. Also we investigate the optimality of row–column designs which satisfy an ‘adjusted orthogonality’ condition. Our point of departure is the paper by Shah, Raghavarao and Khatri (1976) and that of Mitchell and John (1977).     

Planning for permanency planning in foster care

The perceived severity of the problem of “drift” in the foster care system has encouraged agencies nationwide to accept permanency planning, but perhaps with limited attention to the planning process. This article examines the phenomenon of permanency planning from a planning perspective by focusing on: (a) definition of the permanency planning problem, (b) understanding of issues relevant to permanency planning (caseworker, program and systemic), (c) appreciation of the operational context (political, economic and social factors), and (d) evaluation of available options and possible consequences in developing or improving a permanency planning program. Permanency planning is not a simple, ready-made program applicable to any situation; it involves coordinated efforts by workers, service systems, and society in general, and requires time, commitment and resources. Successful planning for permanency planning can permit more children to grow up in homes providing continuity and stability.     

An infinite class of PBIB designs

In this paper, an infinite class of partially balanced incomplete block (PBIB) designs of m+1 associate classes is constructed through the use of a series of row-orthogonal matrices known as partially balanced orthogonal designs (PBOD) of m-associate classes. For the purpose, a series of PBOD is obtained through a method described herein. An infinite class of regular GD designs is also reported.     

Designing two factor experiments for selecting interactions

This paper considers the problem of devising a single stage procedure for selecting the treatment combination associated with the largest interaction in a two-factor rfx× c experiment having independent normally distributed observations with common known variance. The intuitive procedure based on the best linear unbiased estimators of the population interactions is employed. Initially an indifference zone formulation is used; the problem of determining the least favorable configuration is reduced to a nonlinear programming problem with log concave objective function and a convex polytope as feasible region. A solution technique is introduced in the context of an illustrative example. The problem is also considered using a preferred population formulation; this approach requires a strengthened version of the indifference zone probability requirement. It is shown that the same sample size guarantees this requirement as does the earlier one.     

Generalized Youden designs: construction and tables

Generalized Youden Designs are generalizations of the class of two-way balanced block designs which include Latin squares and Youden squares. They are used for the same purposes and in the same way that these classical designs are used, and satisfy most of the common criteria of design optimality.We explicitly display or give detailed instructions for constructing all these designs within a practical range: when υ, the number of treatments, is ?25; and b₁ and b₂, the dimensions of the design array, are each ?50.     

Inferences concerning interpersonal choices given anonymously

It may be difficult or impossible to make a reliable empirical investigation of an unknown network of interpersonal choices or contacts if these are in some respect sensitive for the individuals involved. We consider how choice data given anonymously can be used to reveal various features of the network. Using tools from statistical decision theory and information theory we can determine risks and capacities of disclosing choice data, the graph structure of choice data and its numbers of loops and mutuals. We give some general results, illustrate the combinatorial complexity of the problem and comment upon the computational difficulties.     

Optimal estimation of a finite population mean under generalized random permutation models

Employing certain generalized random permutation models and a general class of linear estimators of a finite population mean, it is shown that many of the conventional estimators are “optimal” in the sense of minimum average mean square error. Simple proofs are provided by using a well-known theorem on UMV estimation. The results also cover certain simple response error situations.     

Projective (2n,n,λ,1)-designs

A projective (2n,n,λ,1)-design is a set $\text{D}$ of n element subsets (called blocks) of a 2n-element set V having the properties that each element of V is a member of λ blocks and every two blocks have a non-empty intersection. This paper establishes existence and non-existence results for various projective (2n,n,λ,1)-designs and their subdesigns.