DISPERSION CONTROL FOR MULTIVARIATE PROCESSES – SOME COMPARISONS

Tang & Barnett (1996) present some techniques for monitoring and controlling the dispersion of multivariate normal processes based on subgroup data. The current paper compares the proposed techniques and various competing procedures. Simulation results indicate that the proposed techniques are superior to existing procedures.     

Statistical issues in the assessment of health outcomes in children: a methodological review

Summary.  The lack of outcome measures that are validated for use on children limits the effectiveness and generalizability of paediatric health care interventions. Statistical epidemiology is a broad concept encompassing a wide range of useful techniques for use in child health outcome assessment and development. However, the range of techniques that are available is often confusing and prohibits their adoption. In the paper an overview of methodology is provided within the paediatric context. It is demonstrated that in many cases assessment can be performed relatively straightforwardly by using standard statistical techniques, although sometimes more sophisticated techniques are required. Examples of both physiological and questionnaire-based outcomes are given. The usefulness of these techniques is highlighted for achieving specific objectives and ultimately for achieving methodological rigour in clinical outcome studies that are performed in the paediatric population.     

Fibre analysis and tangent models

Available methods for deriving tests and confidence regions for multiparameter models seem seriously limited in their feasibility. This paper gives preliminary survey to some developing techniques that use conditioning as a primary device to derive tests and confidence regions. The techniques usefibre analysis which involves the conditional determination of OLS (observed level of significance); the techniques are available initally for models with pivotal quantities and for continuous exponential models. The techniques can then be extended to more general models using the notion of tangent model.     

Calibration of the estimators of variance

This investigation suggests new techniques to calibrate estimators of variance. Estimators of the variance of simple mean, ratio and regression estimators under different sampling schemes are shown to be special cases of the proposed calibration techniques. The approach has more practical use due to recent advances in programming techniques and computational speed. An empirical study has been carried out to address the properties of these proposed strategies.     

Cramér-Rao lower bounds for estimators based on censored data

The Cramér-Rao lower bounds for the variances of unbiased estimators based on censored data are given. Useful techniques of evaluation are then derived for these lower bounds. Examples are given to illustrate these techniques. Small-sample comparisons are made between the resulting lower bounds, the variances of the best linear unbiased estimators, and the variances of unbiased esti-mators which are based on the maximum likelihood estimators.     

Optimization in statistics: an overview

This note presents a brief overview of the applications of optimization techniques in statistics. Optimizing techniques are broadly classified as classical, variational, numerical, and mathematical programming. A brief discussion of their applications to statistics is given, and a few references are provided for illustrative purposes.     

An Application of Vector Time Series Techniques to Macroeconomic Forecasting

A wide variety of time series techniques are now used for generating forecasts of economic variables, with each technique attempting to summarize and exploit whatever regularities exist in a given data set. It appears that many researchers arbitrarily choose one of these techniques. The purpose of this article is to provide an example for which the choice of time series technique appears important; merely choosing arbitrarily among available techniques may lead to suboptimal results.     

Sparse regression techniques in low-dimensional survival data settings

In high-dimensional data settings, sparse model fits are desired, which can be obtained through shrinkage or boosting techniques. We investigate classical shrinkage techniques such as the lasso, which is theoretically known to be biased, new techniques that address this problem, such as elastic net and SCAD, and boosting technique CoxBoost and extensions of it, which allow to incorporate additional structure. To examine, whether these methods, that are designed for or frequently used in high-dimensional survival data analysis, provide sensible results in low-dimensional data settings as well, we consider the well known GBSG breast cancer data. In detail, we study the bias, stability and sparseness of these model fitting techniques via comparison to the maximum likelihood estimate and resampling, and their prediction performance via prediction error curve estimates.     

PARSIMONIOUS PERIODIC TIME SERIES MODELING

This paper studies techniques for fitting parsimonious periodic time series models to periodic data. Large sample standard errors for the parameter estimates in a periodic autoregressive moving‐average time series model under parametric constraints are derived. Likelihood ratio statistics for hypothesis testing are examined. The techniques are applied in modeling daily temperatures at Griffin, Georgia, USA.     

Variable Selection for Qualitative Interactions

In this article we discuss variable selection for decision making with focus on decisions regarding when to provide treatment and which treatment to provide. Current variable selection techniques were developed for use in a supervised learning setting where the goal is prediction of the response. These techniques often downplay the importance of interaction variables that have small predictive ability but that are critical when the ultimate goal is decision making rather than prediction. We propose two new techniques designed specifically to find variables that aid in decision making. Simulation results are given along with an application of the methods on data from a randomized controlled trial for the treatment of depression.     

Directional mixture models and optimal estimation of the mixing density

The authors develop consistent nonparametric estimation techniques for the directional mixing density. Classical spherical harmonics are used to adapt Euclidean techniques to this directional environment. Minimax rates of convergence are obtained for rotation ally invariant densities verifying various smoothness conditions. It is found that the differences in smoothness between the Laplace, the Gaussian and the von Mises‐Fisher distributions lead to contrasting inferential conclusions.     

Time series,point processes,and hybrids

Techniques developed for the study of time series, point processes, and marked point processes can suggest corresponding techniques for each other, and common techniques can be recognized. In this paper connections are drawn based on conceptual foundations, basic parameters, analyses, displays, algorithms, problems, models. The definitions and techniques are brought out by specific scientific problems. The emphasis is on the single-realization stationary case and on the use of second- and third-order moments to help understand the realization. The tool of stacking, at a particular period, is employed in several of the examples.     

Randomized response surveys: Optimum estimation of a finite population total

Randomized response (RR) techniques are used to gather information of a sensitive nature. Optimal sampling strategies for estimating a finite population total under a superpopulation model have been derived by utilizing auxiliary information suitably in constructing the RR techniques.     

Automatic bandwidth selection in robust nonparametric regression

Nonparametric regression techniques have been studied extensively in the literature in recent years due to their flexibility.In addition robust versions of these techniques have become popular and have been incorporated into some of the standard statistical analysis packages.With new techniques available comes the responsibility of using them properly and in appropriate situations. Often, as in the case presented here, model-fitting diagnostics, such as cross-validation statistics,are not available as tools to determine if the smoothing parameter value being used is preferable to some other arbitrarily chosen value.     

A comparison of linear regression techniques in method comparison studies

In this study, the performances of linear regression techniques, which are especially used in clinical chemistry in method comparison studies, are compared via the Monte-Carlo simulation. The regression techniques that take the measurement errors of both dependent and independent variables into account are called Type II regression techniques. In this study, we also compare the performances of Type II and Type I (classical regression techniques that do not take the measurement errors of the independent variable into account) regression techniques for different sample sizes and different shape parameters of the Weibull distribution. The mean square error is used as a performance criterion of each technique. MATLAB 7.02 software is used in the simulation study. As a result, in all conditions, the ordinary least-square (OLS)-bisector regression technique, which bisects the OLS(Y | X) and the OLS(X | Y), shows the best performance.     

Selecting important independent variables in linear regression models

A large body of literature exists on the techniques for selecting the important variables in linear regression analysis. Many of these techniques are ad hoc in nature and have not been studied from a theoretical viewpoint. In this paper we discuss some of the more commonly used techniques and propose a selection procedure based on the statistical selection and ranking approach. This procedure is easy to compute and apply. The procedure depends on the goodness of fit of the model and the total error associated with it.     

Adjustment with three continuous variables

Correlation is not causation. Spurious association between X and Y may be due to a confounding variable W. Statisticians may adjust for W using a variety of techniques. This article presents the results of simulations conducted to assess the performance of these techniques under various, elementary, data-generating processes. The results indicate that no technique is best overall and that specific techniques should be selected based on the particulars of the data-generating process. Here, we show how causal graphs can guide the selection or design of techniques for statistical adjustment. R programs are provided for researchers interested in generalization.     

Response Inducing Techniques for Mail Surveys

A mail survey has been conducted to discover why certain hospital based health professionals did not reply to a previous mail survey. The results will assist in the design of future mail surveys for this group and may have a more general interest and applicability. Two return-inducing techniques have been tested. Other such techniques, reported in the literature but untested in this study, are listed. The observed responses times are reported and some suggestions regarding timing are made.     

Discussion on the paper by Stone

Summary. The single-input case of the 'technical efficiency' theory of M. J. Farrell is reformulated geometrically and algebraically. Its linear programming developments as 'data envelopment analysis' are critically reviewed, as are the related techniques of 'stochastic frontier analysis'. The sense and realism of using data envelopment analysis or stochastic frontier analysis techniques, rather than some value-based method, for the assessment of police force efficiency are questioned with reference to the Spottiswoode report and related studies.