Avancerad nivå (master)

Generaliserade linjära modeller (ht 24)
Inferensteori (ht 24)
Statistiska beräkningar (vt 25)
Vetenskapsteori (vt 25)

 

Doktorandnivå

Statistical inference, delad med Krzysctof Podgorski, Lunds universitet  (vt 25)

Mina huvudsakliga forskningsintressen är urvalsteori och asymptotik generellt inom statistisk inferens. Jag har också arbetat en hel del med didaktik.

Inom urvalsteori har jag i det senaste koncentrerat forskningen på bortfallsproblem genom kalibrering. Nu arbetar jag med variansskattningar för kalibreringsskattningar.

Ett annat problem som jag ägnat mycket tid åt de senaste åren är hur man ska konstruera konfidensintervall för binomialfördelningens p.
Slutsats: Använd aldrig Waldintervallet!

Se några av de senaste publikationerna under "Publikationer".

I urval från Stockholms universitets publikationsdatabas

• “Optimal” calibration weights under unit nonresponse in survey sampling

  2019. Per Gösta Andersson. Survey Methodology 45 (3), 533-542

  Artikel

  High nonresponse is a very common problem in sample surveys today. In statistical terms we are worried about increased bias and variance of estimators for population quantities such as totals or means. Different methods have been suggested in order to compensate for this phenomenon. We can roughly divide them into imputation and calibration and it is the latter approach we will focus on here. A wide spectrum of possibilities is included in the class of calibration estimators. We explore linear calibration, where we suggest using a nonresponse version of the design-based optimal regression estimator. Comparisons are made between this estimator and a GREG type estimator. Distance measures play a very important part in the construction of calibration estimators. We show that an estimator of the average response propensity (probability) can be included in the “optimal” distance measure under nonresponse, which will help to reduce the bias of the resulting estimator. To illustrate empirically the theoretically derived results for the suggested estimators, a simulation study has been carried out. The population is called KYBOK and consists of clerical municipalities in Sweden, where the variables include financial as well as size measurements. The results are encouraging for the “optimal” estimator in combination with the estimated average response propensity, where the bias was reduced for most of the Poisson sampling cases in the study.

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• Approximate Confidence Intervals for a Binomial p—Once Again

  2022. Per Gösta Andersson. Statistical Science 37 (4), 598-606

  Artikel

  The problem of constructing a reasonably simple yet wellbehaved confidence interval for a binomial parameter p is old but still fascinating and surprisingly complex. During the last century, many alternatives to the poorly behaved standard Wald interval have been suggested. It seems though that the Wald interval is still much in use in spite of many efforts over the years through publications to point out its deficiencies. This paper constitutes yet another attempt to provide an alternative and it builds on a special case of a general technique for adjusted intervals primarily based on Wald type statistics. The main idea is to construct an approximate pivot with uncorrelated, or nearly uncorrelated, components. The resulting AN (Andersson–Nerman) interval, as well as a modification thereof, is compared with the well-renowned Wilson and AC (Agresti–Coull) intervals and the subsequent discussion will in itself hopefully shed some new light on this seemingly elementary interval estimation situation. Generally, an alternative to the Wald interval is to be judged not only by performance, its expression should also indicate why we will obtain a better behaved interval. It is argued that the well-behaved AN interval meets this requirement.

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• The Wald Confidence Interval for a Binomial p as an Illuminating “Bad” Example

  2023. Per Gösta Andersson. American Statistician 77 (4), 443-448

  Artikel

  When teaching we usually not only demonstrate/discuss how a certain method works, but, not less important, why it works. In contrast, the Wald confidence interval for a binomial p constitutes an excellent example of a case where we might be interested in why a method does not work. It has been in use for many years and, sadly enough, it is still to be found in many textbooks in mathematical statistics/statistics. The reasons for not using this interval are plentiful and this fact gives us a good opportunity to discuss all of its deficiencies and draw conclusions which are of more general interest. We will mostly use already known results and bring them together in a manner appropriate to the teaching situation. The main purpose of this article is to show how to stimulate students to take a more critical view of simplifications and approximations. We primarily aim for master’s students who previously have been confronted with the Wilson (score) interval, but parts of the presentation may as well be suitable for bachelor’s students. 

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• A Note on Confidence Intervals for a Binomial p: Andersson–Nerman vs. Wilson

  2024. Per Gösta Andersson. Stat 13 (4)

  Artikel

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