Department of Economics / Institutionen för nationalekonomi med statistik

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HOUSEHOLD MARKET AND NON-MARKET ACTIVITIES - DESIGN ISSUES FOR A PILOT STUDY
(University of Gothenburg, 1982-02-01) Johnsson, Tommy
The design of a pilot survey of households is the subject of this paper. A few methods to collect data about consumption expenditures and time-use are to be compared. Among the design issues are the allocation of the sample on experimental groups, on strata and on days of the week.
ANALYS AV KATEGORISKA DATA - En metodstudie i anslutning till statsvetenskaplig forskning.
(University of Gothenburg, 1983-02-01) Eriksson, Sven
Inriktningen av denna rapport har praglats av forfattarens erfarenheter fran arbetet med urvalsplaner for delundersokningarna avseende medborgare, politiker och tjansteman inom forskningsprogrammet 1979-1981 for utvardering av kommunsammanslagningsreformen, av allmanna diskussioner om analysproblem under planeringsstadiet av forskningsprogrammet samt av studium av de olika projektens slutrapporter. I forskningsprogrammet deltog for skare fran samtliga statsvetenskapliga institutioner i Sverige med undantag for Uppsala samt forskare fran kulturgeografiska institutionen i Lund. 1 Forfattaren har redovisat urvalsplaner och estimation av populationskarakteristikor samt diskuterat vissa allmanna metodfragor i tidigare rapporter (T 4 ], [26] bilagor samt [28] kap. 12).
NONPARAMETRIC REGRESSION WlITH SIMPLE CURVE CHARACTERISTICS
(University of Gothenburg, 1985-04-01) Holm, Sture; Frisen, Marianne
The character of nonparametric statistical methods is that they are constructed for very general situations, without the specific narrow assumptions, which appear in the common parametric methods. Isotonic regression is a nonparametric regression method, which has paid a well deserved attention for some decades. In this case the only assumption about the regression function is that it is non-decreasing (or non increasing). The basic theory of isotonic regression is contained in the book by Barlow, Bartholomew, Brenner and Brunk (1972).
MULTIPLE COMPARISON TESTS BASED ON THE BOOTSTRAP
(University of Gothenburg, 1986-01-01) Johnsson, Tommy
A multiple test procedure for pairwise comparisons based on the bootstrap is presented. It is a stagewise test without any distributional assumptions. It is also very general according to the number and types of hypotheses to be tested. The procedure is evaluated and to some extent compared to existing procedures. A FORTRAN computer program is available for the practical performance of the procedure suggested.
PARAMETRIC AND NONPARAMETRIC TESTS FOR BXOEQUIVALENCE TRIALS
(University of Gothenburg, 1986-02-01) Dahlbom, Ulla; Holm, Sture
In pharmacology, comparison of bioavailability is an important problem. A new formulation of a drug is compared with a standard formulation in human subjects. When the extent of absorption is studied the areas under the concentration/time curves (AUC) are the statistics used for analysis. These statistics are determined by some parametric or nonparamatric methods from the basic concentration measurements e. g. every half hour during a day.
A TIPPETT-ADAPTIVE METHOD OF COMBINING INDEPENDENT STATISTICAL TESTS.
(University of Gothenburg, 1986-03-01) Westberg, Margareta
A new procedure is proposed in order to combine the information of P-values obtained from several independent tests in order to test an overall hypothesis. The test statistic of this new procedure is of the same type as Tippett's since in each step one of the P-values is compared with a constant. This new procedure is adaptive in the sense that the choice of P-value depends on the data. The procedure is very simple and in the performed examination this method is better than Tippett's in almost all situations. Thus this new "Tippett-adaptive" method is a good alternative to Tippett's procedure.
TESTING THE APPROXIMATE AGREEMENT WITH A HYPOTHESIS
(University of Gothenburg, 1986-05-01) Frisén, Marianne
When statistical tests are applied it is often known beforehand that the hypotheses would be rejected with sufficiently narge sample sizes. This happens whenever hypotheses is not exactly true but only approximately true. Some attempts of solution of this dilemma are discussed and exemplified with test of bioequivalence. One of these, powerfunction analysis, is applied on preparatory tests. In that case the approximate agreement with some condition (e.g. normal distribution) for the main analysis (e.g., t -test) is tested.
ABSTRACT BOOTSTRAP CONFIDENCE INTERVALS IN LINEAR MODELS
(University of Gothenburg, 1990-01-01) Holm, Sture
A bootstrap method for generating confidence intervals in linear models is suggested. The method is motivated by an abstract nonobservable bootstrap sample of true residuals leading to an observable final result. This means that the only error in the method is the pure bootstrap error obtained by replacing the true residual distribution by the empirical one. It is shown that the method is valid, having the same asymptotic conditional distribution as the ordinary bootstrap method. Simulations indicate clearly that the abstract bootstrap method works better than the ordinary bootstrap method for small samples.
ON TESTS OF EQUIVALENCE
(University of Gothenburg, 1990-02-01) Holm, Sture; Dahlbom, Ulla
We will study here a general method for constructing equivalence tests for problems with onedimensional or multidimensional parameter. In the biometric field, the equivalence tests have been studied by many authors under the name of bioequivalence methods. Our general method is closely related to a method for acceptance sampling in the multiparameter case by Berger (1982) and a bioequivalence test method by Schuirmann (1981) for normal distributions and onedimensional parameter. We combine in a general form the ideas of two-sidedness by Schuirmann (1981) and the ideas for multiparameter handling by Berger (1982). We give a number of parametric and nonparametric examples where the general method is used and we illustrate the methods power properties by simulation results.
ON SOME PREDICTION METHODS FOR CATEGORICAL DATA
(University of Gothenburg, 1991-01-01) Olofsson, Jonny
Good prediction methods are important in many fields where qualitative variables are involved. The criterion of a good prediction method, used in this paper, is the average mean squared error. This criterion is used to compare and derive prediction methods, when the variable of interest is binary. The methods considered here are based on the maximumlikelihood estimators of the expectation of the binary varible, for which we want to make a prediction. Derivations and simulations are made for the case where we have one qualitative background variable. It is for example demonstrated that, when the ordinary chi-squared test is used for choosing between two prediction methods, it should not be adopted on a conventional low level of significance (e.g. 5%).
ON THE PROBLEM OF OPTIMAL INFERENCE IN THE SIMPLE ERROR COMPONENT MODEL FOR PANEL DATA
(University of Gothenburg, 1991-02-01) Jonsson, Robert
For data consisting of cross sections of units observed over time, the Error Component Regression (ECR) model, with random intercept and constant slope, may sometimes be adequate. While most interest has been focused on pOint estimation of the slope parameter S, little attention has been paid to the problem of making confidence statements and tests about S. In this paper, the performance of some estimators of S and the corresponding test statistics are investigated. In consideration of bias, efficiency and power of tests, it is shown that the Maximum Likelihood estimator with the cqrresponding test statistic is outstanding in large samples. But, in the small sample case there are hardly any reasons for the Maximum Likelihood approach. In the latter case, the use of estimators and test statistics based on within- or between group comparisons is suggested. The results, together with tools for a proper application of the ECR model, are demonstrated on data from a medical follow-up study.
STATISTICAL RANK METHODS FOR ORDINAL CATEGORICAL DATA
(University of Gothenburg, 1991-03-01) Holm, Sture; Svensson, Elisabeth
The aim of this paper is to present a new rank. method for analysing ordinal scale problems, and to give some of its basic properties. The method is suitable for the assessment of validity and reliability of health measurement instruments. We will be able to separate systematic and random differences between judges or scales and also. in a suitable way. measure the size of these two types of differences. Some methods for estimating systematic differences between raters will be given. The model is illustrated in a worked example.
ASPECTS OF MODELLING NONLINEAR TIME SERIES
(University of Gothenburg, 1992-01-01) Teräsvirta, Timo; Tjøstheim, Dag; Granger, Clive W J
It is common practice for economic theories to postulate non-linear relationships between economic variables, production functions being an example. If a theory suggests a specific functional form, econometricians can propose estimation techniques for the parameters, and asymptotic results, about normality and consistency, under given conditions are known for these estimates, see e.g. Judge et. a1. (1985) and White (1984) and Gallant (1987, chapter 7). However, in many cases the theory does not provide a single specification or specifications are incomplete and may not capture the major features of the actual data, such as trends, seasonality or the dynamics. When this occurs, econometricians can try to propose mort: general specifications and tests of them. There are clearly an immense number of possible parametric nonlinear models and there are also many nonparametric techniques for approximating them. Given the limited amount of data that is usually available in economics it would not be appropriate to consider many alternative models or to use many techniques. Because of the wide possibilities the methods and models available to analyze non-linearities are usually very flexible so that they can provide good approximations to many different generating mechanisms. A consequence is that with fairly small samples the methods arc inclined to over-fit, so that if the true mechanism is linear, say, with residual variance the fitted model may appear to find nonlinearity and the estimated residual variance is less than . The estimated model will then be inclined to forecast badly in the post-sample period. It is therefore necessary to have a specific research strategy for modelling non-linear relationships between time series. In this chapter the modelling process concentrates on a particular situation, where there is a single dependent variable Yt to be explained and:.!..t is a vector of exogenous variables.
Exact Semiparametric Inference About the Within-Subject Variability in 2 x 2 Crossover Trails.
(University of Gothenburg, 1992-03-01) Guilbaud, Olivier
The comparison of primary interest in a 2 x 2 crossover trial typically concerns the effect of the treatments, say A and B, on the mean response level. This article deals with another important aspect, namely the within-subject response variability under A and B. Differences in drug formulation and/or administration may lead to considerable differences in withinsubject variability, whatever is the difference in terms of mean level; and consideration of both these aspects may therefore be of considerable importance for the judgement of the treatments. It is shown that, although there are no within-subject treatment replications, it is possible to make various exact inferences about the AlB ratio of within-subject variances and about the (A - B)difference in mean level, simultaneously and marginally. These inferences are semiparametric in that no distributional assumption is made about the betweensubject variability, whereas a normality assumption is used for the within-subject variability. The inferences include tests, confidence regions, and a multiple test procedure. A power approximation is also given. The results are illustrated numerically.
SEPARATION OF SYSTEMATIC AND RANDOM ERRORS IN ORDINAL RATING SCALES
(University of Gothenburg, 1992-04-01) Svensson, Elisabeth; Holk, Sture
The aim of this paper is to introduce a new rank method which enables us to separate the inconsistency of repeated measurements into random and systematic differences and to quantify this lack of consistency in a few measures. The key of the separation approach is to make a particular type of ranking of the repeated judgements in the same experimental unit. It means that cases which have the same classification from one rater will be internally ranked according to the classifications from the other. This enables us to extract the random variation. The variance of the rank differences between the judgements is a suitable measure of the random interrater variability. The systematic differences are described by empirical measures of relative position and of relative concentration. These measures are normed into the interval [-1,1]. Our method has been applied to several medical rating scales both for construction and analysis. We use one of the data sets as an illustration.
COMPARISON BETWEEN TWO METHODS OF SURVEILLANCE: EXPONENTIALLY WEIGHTED MOVING AVERAGE VS CUSUM
(University of Gothenburg, 1993-01-01) Frisén, Marianne; Åkermo, Göran
When control charts are used in practice it is necessary to know the characteristics of the charts in order to know which action is appropriate at an alarm. The probability of a false alarm, the probability of successful detection and the predictive value are three measures (besides the usual ARL) used for comparing the performance of two methods often used in surveillance systems. One is the "Exponentially weighted moving average" method, EWMA, (with several variants) and the other one is the CUSUM method (Vmask). Illustrations are presented to explain the observed differences. It is demonstrated that a high probability of alarm in the beginning (although it gives good ARL properties) might cause difficulties since a low predicted value makes action redundant at early alarms.
EXACT PROPERTIES OF McNEMAR'S TEST IN SMALL SAMPLES
(University of Gothenburg, 1993-02-01) Jonsson, Robert
The exact distribution of McNemar's test statistic is used to determine critical pOints for two-sided tests of equality of marginal proportions in the correlated 2x2 table. The result is a conservative unconditional test which reduces to the conditional binomial test as a special case. Exact critical points are given for the significance levels 0.05, 0.01 and 0.001 with the sample sizes n=6(1)50. A computer program for tail probabilities makes the calculation of power easy. It is concluded that McNemar's test is never inferior to the conditional binomial test and that much can be gained by using the McNemar test if the main purpose is to detect differences between the marginal proportions in small samples. A further conclusion is that the chi-square approximation of McNemar's test statistic may be inadequate when n<=50. Especially the 5% critical points are constantly too small.
RESAMPLING PROCEDURES IN LINEAR MODELS
(University of Gothenburg, 1993-03-01) Gellerstedt, Martin
We will study here different resampling procedures for creating confidence sets in linear models. A special technique called abstract resampling makes it possible to use the true residuals and the true model for resampling. This may seem to be peculiar since the true residuals contains unknown parameters and thus are non observable; but for each specified parameter value the residuals are observable and can be used for resampling. Furthermore simulating the null distribution of some appropriate statistic gives the possibility to test the accuracy of a hypothetic parameter value. Finally a confidence set can be created by finding the parameter values which can not be rejected. Bootstrapping the true residuals will be called abstract bootstrapping. We will show that the abstract bootstrap method is closely related to a permutation method. A balanced abstract bootstrap method will also be presented, a method which treats the grand mean in linear models and can be applied in ordinary bootstrapping as well. The resampling methods; bootstrap, abstract bootstrap and the permutation method are all closely related. Which method to use is discussed from a practical point of view.
Statistical surveillance of business cycles
(University of Gothenburg, 1994-01-01) Frisén, Marianne
Methods for timely detection of turning-points in business cycles are discussed from a statistical point of view. The theory on optimal surveillance is used to characterize different approaches advocated in literature. This theory is also used to derive a new method for nonparametric detection of turning-points. It utilizes the characteristics of monotonic and unimodal regression. Estimation of parameters m a more or less stable model is thus avoided. Different new ways to evaluate methods are used and discussed. The principles are illustrated by data from Sweden and the USA.
CHARACTERIZATION OF METHODS FOR SURVEILLANCE BY OPTIMALITY
(University of Gothenburg, 1994-02-01) Frisén, Marianne
Different criteria of optimality are discussed. The shortcomings of some earlier criteria of optimality are demonstrated by their implications. The correspondences between some criteria of optimality and some methods are examined. The situations under which some commonly used methods have a certain optimality are thus illuminated. Linear approximations of the LR (likelihood ratio) method, which satisfies several criteria of optimality, are presented. These linear approximations are used for comparisons with other linear methods, especially the EWMA (exponentially weighted moving average) method. These comparisons specify the situations for which the linear methods can be regarded as approximations of the LR method.

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