Change search
Refine search result
1 - 6 of 6
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the 'Create feeds' function.
  • 1. Franzen, Karin
    et al.
    Johansson, Jan-Erik
    Andersson, Gunnel
    Pettersson, Nicklas
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nilsson, Kerstin
    Urinary incontinence in women is not exclusively a medical problem: A population-based study on urinary incontinence and general living conditions2009In: Scandinavian Journal of Urology and Nephrology, ISSN 0036-5599, E-ISSN 1651-2065, Vol. 43, no 3, p. 226-232Article in journal (Refereed)
    Abstract [en]

    Objective. The aim of the study was to analyse differences in general health and general living conditions between women with and without urinary incontinence (UI). Material and methods. This cross-sectional population-based study was conducted in Orebro County, Sweden. A public health questionnaire, Life and Health, was sent to a randomly selected sample of the population. The questionnaire consisted of 87 questions on broad aspects of general and psychiatric health. An additional questionnaire was enclosed for those respondents who reported experiencing UI. The data were analysed using binary logistic regression. The final study population constituted 4609 women, 1332 of whom had completed both questionnaires. The remaining 3277 had completed only the Life and Health questionnaire. Effect measures were odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Results. Statistically significant associations were found between UI and the occurrence of musculoskeletal pain (OR 1.45, 95% CI 1.20-1.76), fatigue and sleeping disorders (OR 1.59, 95% CI 1.30-1.95), feelings of humiliation (OR 1.29, 95% CI 1.12-1.50), financial problems (OR 1.36, 95% CI 1.11-1.66), and reluctance to seek medical care (OR 1.43, 95% CI 1.21-1.68). Conclusion. UI among women is commonly associated with a number of different psychosocial problems as well as an expressed feeling of vulnerability.

  • 2.
    Pettersson, Nicklas
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Bias Reduction Of Finite Population Imputation By Kernel MethodsIn: Statistics in Transition, ISSN 1234-7655Article in journal (Refereed)
    Abstract [en]

    Missing data is a nuisance in statistics. Real donor imputation can be used with item nonresponse. A pool of donor units with similar values on auxiliary variables is matched to each unit with missing values. The missing value is then replaced by a copy of the corresponding observed value from a randomly drawn donor. Such methods can to some extent protect against nonresponse bias. But bias also depends on the estimator and the nature of the data. We adopt techniques from kernel estimation to combat this bias. Motivated by Pólya urn sampling, we sequentially update the set of potential donors with units already imputed, and use multiple imputations via Bayesian bootstrap to account for imputation uncertainty. Simulations with a single auxiliary variable show that our imputation method performs almost as well as competing methods with linear data, but better when data is nonlinear, especially with large samples.

  • 3.
    Pettersson, Nicklas
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Informed kernel imputationArticle in journal (Refereed)
  • 4.
    Pettersson, Nicklas
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Kernel imputation with multivariate auxiliariesArticle in journal (Refereed)
  • 5.
    Pettersson, Nicklas
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Multiple Kernel Imputation: A Locally Balanced Real Donor Method2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    We present an algorithm for imputation of incomplete datasets based on Bayesian exchangeability through Pólya sampling. Each (donee) unit with a missing value is imputed multiple times by observed (real) values on units from a donor pool. The donor pools are constructed using auxiliary variables. Several features from kernel estimation are used to counteract unbalances that are due to sparse and bounded data. Three balancing features can be used with only one single continuous auxiliary variable, but an additional fourth feature need, multiple continuous auxiliary variables. They mainly contribute by reducing nonresponse bias. We examine how the donor pool size should be determined, that is the number of potential donors within the pool. External information is shown to be easily incorporated in the imputation algorithm. Our simulation studies show that with a study variable which can be seen as a function of one or two continuous auxiliaries plus residual noise, the method performs as well or almost as well as competing methods when the function is linear, but usually much better when the function is nonlinear.

  • 6.
    Pettersson, Nicklas
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Real donor imputation pools2012In: Proceedings of the Workshop of the Baltic-Nordic-Ukrainian network on survey statistics, 2012 / [ed] Mārtiņš Liberts, Valmiera, 2012, p. 162-168Conference paper (Other academic)
    Abstract [en]

    Real donor matching is associated with hot deck imputation. Aux-iliary variables are used to match donee units with missing values to aset of donor units with observed values, and the donee missing valuesare ‘replaced’ by copies of the donor values, as to create completelyfilled in datasets. The matching of donees and donors is complicatedby the fact that the observed sample survey data is often both sparseand bounded. The important choice of how many possible donors tochoose from involves a trade-off between bias and variance. We trans-fer concepts from kernel estimators to real donor imputation. In asimulation study we show how bias, variance and the estimated vari-ance of a population behaves, focusing on the size of donor pools.

1 - 6 of 6
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf