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Haug, Ola; Bolin, David; Frigessi, Arnoldo; Guttorp, Peter; Orskaug, Elisabeth & Scheel, Ida
[Show all 7 contributors for this article]
(2016).
Modelling and predicting residential water damage insurance claims via a calibrated dynamical downscaling.
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Haug, Ola; Frigessi, Arnoldo; Scheel, Ida & Guttorp, Peter
(2015).
Modelling and predicting residential water damage insurance claims in a climate change perspective.
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Haug, Ola; Scheel, Ida; Orskaug, Elisabeth; Frigessi, Arnoldo; Guttorp, Peter & Ferkingstad, Egil
(2014).
Vulnerability models for water damage insurance claims - predictions of future losses in a climate change perspective.
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Scheel, Ida; Haug, Ola; Orskaug, Elisabeth; Frigessi, Arnoldo & Guttorp, Peter
(2012).
Evaluating and Calibrating Dynamically Downscaled Precipitation Using the Doksum Shift Function.
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Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Maraun, Douglas & Guttorp, Peter
(2012).
Evaluation and calibration of dynamically downscaled precipitation over Norwegian mainland.
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Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Maraun, Douglas & Guttorp, Peter
(2012).
Evaluation and Calibration of Dynamically Downscaled Precipitation over Norwegian Mainland.
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Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Guttorp, Peter & Maraun, Douglas
(2011).
Calibrating dynamically down-scaled precipitation using the Doksum shift function.
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Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Guttorp, Peter; Haugen, Jan Erik & Tveito, Ole Einar
[Show all 7 contributors for this article]
(2011).
Evaluation of a dynamic downscaling of Norwegian precipitation.
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Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Guttorp, Peter & Maraun, Douglas
(2011).
Calibrating dynamically downscaled precipitation using the Doksum shift function.
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Scheel, Ida; Green, Peter J & Rougier, Jonathan C
(2011).
A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models.
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Scheel, Ida; Green, Peter J & Rougier, Jonathan C
(2010).
A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models.
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Scheel, Ida; Green, Peter J & Rougier, Jonathan C
(2010).
A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models.
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Scheel, Ida; Green, Peter J & Rougier, Jonathan C
(2009).
A graphical diagnostic for identifying influential model choices in Bayesian hierarchical models.
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Scheel, Ida; Green, Peter J & Rougier, Jonathan C
(2008).
Identifying influential model choices in Bayesian hierarchical models.
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Scheel, Ida; Green, Peter J & Rougier, Jonathan C
(2008).
Identifying influential model choices in Bayesian hierarchical models.
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Lyngstad, Trude M.; Scheel, Ida & Jansen, Peder A.
(2007).
Hvordan spres infeksiøs lakseanemi (ILA)?
Norsk Fiskeoppdrett.
ISSN 0332-7132.
32(9),
p. 52–55.
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Scheel, Ida; Aldrin, Magne; Glad, Ingrid Kristine; Sørum, R.; Lyng, Heidi & Frigessi, Arnoldo
(2006).
The influence of missing value imputation on the detection of differentially expressed genes from microarray data.
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Parr, Christine Louise; Hjartåker, Anette; Scheel, Ida; Laake, Petter; Lund, Eiliv & Veierød, Marit Bragelien
(2006).
Substituting missing values in food frequency questionnaires (FFQs): effects on energy intake in the Norwegian Women and Cancer Study (NOWAC).
Show summary
Objective: Missing values are common in FFQs used in large epidemiological studies and must be handled when calculating energy intake. We compare results from different methods for substituting/imputing missing values using NOWAC data.
Methods: A FFQ was mailed twice (test-retest) to 1995 women aged 46-75 y from the NOWAC cohort in a reproducibility study (75% response). Missing answers in the test were imputed as follows:
1. Frequencies=0 (null intake) and portion sizes=smallest
2. Mode values
3. Median values
4. Retest values. Remaining missing values were treated as in method 1
5. K-nearest neighbors imputation using a weighted average of the values for the same question from the K=10 most similar respondents within the same dataset.
Results: After imputation of the test FFQ (17% missing data) with the different methods, estimated mean energy intake (MJ/day) with 95% CI was:
1. 6.43 (6.34, 6.53)
2. 6.92 (6.83, 7.01)
3. 7.16 (7.06, 7.25)
4. 6.93 (6.84, 7.03)
5. 7.52 (7.42, 7.62)
Conclusion: Exclusion is not practical when most respondents have missing values. However, the calculated energy intake is influenced by the imputation method. Treating missing as null intake may not be correct for all foods and may lead to underestimation of energy intake.
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Scheel, Ida; Aldrin, Magne; Glad, Ingrid Kristine; Sørum, R.; Lyng, Heidi & Frigessi, Arnoldo
(2005).
The influence of missing value imputation on the detection of differentially expressed genes from microarray data.
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Scheel, Ida; Aldrin, Magne; Glad, Ingrid Kristine; Sørum, Ragnhild; Lyng, Heidi & Frigessi, Arnoldo
(2005).
The influence of missing value imputation on the detection of differentially expressed genes from microarray data.
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Parr, Christine Louise; Hjartåker, Anette; Scheel, Ida; Laake, Petter; Lund, Eiliv & Veierød, Marit Bragelien
(2005).
Substituting missing values in food frequency questionnaires (FFQs): effects on energy intake in the Norwegian Women and Cancer Study.
Show summary
Objective: Missing values are common in self-administered FFQs used in large epidemiological studies and must be handled when calculating energy and nutrient intake. We compare results from different methods for substituting/imputing missing values using data from the Norwegian Women and Cancer study (NOWAC). We include K-nearest neighbors imputation (KNNimpute). This is a widely used method for missing entries in cDNA microarray data, but is here extended and adapted to FFQ data, which is new to our knowledge.
Methods: A FFQ designed to assess habitual diet was mailed twice (test-retest 3 months apart) to a random sample of 1995 women aged 46-75 y from the NOWAC cohort as part of a reproducibility study (75% response). A total of 126 questions were included in the dietary intake calculations. Missing answers to consumption frequencies and portion sizes in the test FFQ were imputed before the calculations using the following methods:
1. Frequencies=0 (null intake) and portion sizes=smallest for a conservative estimate
2. The mode value
3. The median value
4. Non-missing values in the retest. Remaining missing values were treated as in method 1
5. KNNimpute using a weighted average of the values for the same question from the K=10 most similar respondents within the same dataset (test). Similarity is evaluated from the closeness of responses to the other FFQ questions.
Results: In the test FFQ 17% of the data matrix was missing. Among respondents (n=1495) 95% had ≥1 missing answer. After imputation with retest values (method 4), 10% of the data was still missing and treated as in method 1. The estimated mean energy intake (MJ/day) with 95% CI after imputation according to the different methods was:
1. 6.43 (6.34, 6.53) for null intake and the smallest portion
2. 6.92 (6.83, 7.01) for mode
3. 7.16 (7.06, 7.25) for median
4. 6.93 (6.84, 7.03) for retest
5. 7.52 (7.42, 7.62) for KNNimpute
Conclusion: Exclusion is not practical when most respondents have missing values. However, the calculated energy intake is influenced by the imputation method used. Missing is frequently handled as null intake, but this may not be correct for all foods. This is indicated by the increase in energy when missing is imputed by the other methods, including the retest values from the same respondents 3 months later. KNNimpute gave the highest energy intake, and will be investigated further.
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Scheel, Ida
(2004).
Spread of infectious agents in salmon farming.
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Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel & Meze-Hausken, Elisabeth
(2011).
A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims. Derivation of distributions and MCMC sampling schemes.
Matematisk Institutt, UiO.
Full text in Research Archive
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Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Guttorp, Peter; Haugen, Jan Erik & Tveito, Ole Einar
[Show all 7 contributors for this article]
(2010).
Supplemental material to: Evaluation of a dynamic downscaling of Norwegian precipitation.
Norsk Regnesentral.
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Scheel, Ida; Green, Peter J & Rougier, Jonathan C
(2010).
Applications of the Local critique plot.
Department of Mathematics, University of Oslo.
ISSN 0806-3842.
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Scheel, Ida; Green, Peter J & Rougier, Jonathan C
(2008).
Identifying influential model choices in Bayesian hierarchical models.
Department of Mathematics, University of Bristol.
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Frigessi, Arnoldo & Scheel, Ida
(2002).
A first statistical methods for the geofraphic localisation of GSM mobile phones.