Ida Scheel

• Parviero, Riccardo; Hellton, Kristoffer Herland; Canright, Geoffrey & Scheel, Ida (2024). STAR: Spread of innovations on graph structures with the Susceptible-Tattler-Adopter-Removed model. Stat. ISSN 2049-1573. 13(2). doi: 10.1002/sta4.671.
• Hjort, Anders Dahl; Scheel, Ida; Sommervoll, Dag Einar & Pensar, Johan (2023). Locally interpretable tree boosting: An application to house price prediction. Decision Support Systems. ISSN 0167-9236. 178. doi: 10.1016/j.dss.2023.114106. Full text in Research Archive
• Parviero, Riccardo; Hellton, Kristoffer Herland; Haug, Ola; Engø-Monsen, Kenth; Rognebakke, Hanne Therese Wist & Canright, Geoffrey [Show all 8 contributors for this article] (2022). An agent-based model with social interactions for scalable probabilistic prediction of performance of a new product. International Journal of Information Management Data Insights. 2(2). doi: 10.1016/j.jjimei.2022.100127. Full text in Research Archive Show summary

  Understanding the spreading process of new products provides valuable knowledge that can be used for effective marketing. The ability to make early prediction of success or failure is a great advantage in innovation processes. Extending current literature in a novel way, we propose a data-driven agent-based methodology that accomplishes this task. Inference and predictions are based on short-time observations of the product adoption history and knowledge of the social network of consumers. We model and predict adoptions at the agent level as driven by unobserved peer-to-peer influence and external factors such as marketing. The method compares interaction between consumers and general campaigns, and quantifies the importance of characteristics of customers and their social relations. Our computationally efficient method is demonstrated by analyzing real data, predicting the process far into the future using data from a short period after launch, and validated by simulation experiments on a true full-scale communication network.

• Hjort, Anders Dahl; Pensar, Johan; Scheel, Ida & Sommervoll, Dag Einar (2022). House price prediction with gradient boosted trees under different loss functions. Journal of Property Research. ISSN 0959-9916. 39(4), p. 333–364. doi: 10.1080/09599916.2022.2070525. Full text in Research Archive Show summary

  Many banks and credit institutions are required to assess the value of dwellings in their mortgage portfolio. This valuation often relies on an Automated Valuation Model (AVM). Moreover, these institutions often report the models accuracy by two numbers: The fraction of predictions within ±20% and ±10% range from the true values. Until recently, AVMs tended to be hedonic regression models, but lately machine learning approaches like random forest and gradient boosted trees have been increasingly applied. Both the traditional approaches and the machine learning approaches rely on minimising mean squared prediction error, and not the number of predictions in the ±20% and ±10% range. We investigate whether introducing a loss function closer to the AVMs actual loss measure improves performance in machine learning approaches, specifically for a gradient boosted tree approach. This loss function yields an improvement from 89.4% to 90.0% of predictions within ±20% of the true value on a data set of N=126719 transactions from the Norwegian housing market between 2013 and 2015, with the biggest improvements in performance coming from the lower price segments. We also find that a weighted average of models with different loss functions improves performance further, yielding 90.4% of the observations within ±20% of the true value.

• Kvamme, Håvard; Borgan, Ørnulf & Scheel, Ida (2019). Time-to-Event Prediction with Neural Networks and Cox Regression. Journal of machine learning research. ISSN 1532-4435. 20(129), p. 1–30. Full text in Research Archive
• Liu, Qinghua; Reiner, Andrew Henry; Frigessi Di Rattalma, Arnoldo & Scheel, Ida (2019). Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows model. Knowledge-Based Systems. ISSN 0950-7051. p. 1–12. doi: 10.1016/j.knosys.2019.104960. Full text in Research Archive
• Gåsemyr, Jørund Inge & Scheel, Ida (2019). Alternatives to post-processing posterior predictive p-values. Scandinavian Journal of Statistics. ISSN 0303-6898. 46(4), p. 1252–1273. doi: 10.1111/sjos.12393. Full text in Research Archive
• Liu, Qinghua; Crispino, Marta; Scheel, Ida; Vitelli, Valeria & Frigessi Di Rattalma, Arnoldo (2019). Model-based learning from preference data. Annual Review of Statistics and Its Application. ISSN 2326-8298. 6, p. 329–354. doi: 10.1146/annurev-statistics-031017-100213.
• Bolin, David; Frigessi, Arnoldo; Guttorp, Peter; Haug, Ola; Orskaug, Elisabeth & Scheel, Ida [Show all 7 contributors for this article] (2016). Calibrating regionally downscaled precipitation over Norway through quantile-based approaches. Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO). ISSN 2364-3579. 2, p. 39–47. doi: 10.5194/ascmo-2-39-2016. Full text in Research Archive Show summary

  Dynamical downscaling of earth system models is intended to produce high-resolution climate information at regional to local scales. Current models, while adequate for describing temperature distributions at relatively small scales, struggle when it comes to describing precipitation distributions. In order to better match the distribution of observed precipitation over Norway, we consider approaches to statistical adjustment of the output from a regional climate model when forced with ERA-40 reanalysis boundary conditions. As a second step, we try to correct downscalings of historical climate model runs using these transformations built from downscaled ERA-40 data. Unless such calibrations are successful, it is difficult to argue that scenario-based downscaled climate projections are realistic and useful for decision makers. We study both full quantile calibrations and several different methods that correct individual quantiles separately using random field models. Results based on cross-validation show that while a full quantile calibration is not very effective in this case, one can correct individual quantiles satisfactorily if the spatial structure in the data are accounted for. Interestingly, different methods are favoured depending on whether ERA-40 data or historical climate model runs are adjusted.

• Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel & Meze-Hausken, Elisabeth (2013). A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims. The Journal of the Royal Statistical Society, Series C (Applied Statistics). ISSN 0035-9254. 62, p. 85–100. doi: 10.1111/j.1467-9876.2012.01039.x. Show summary

  Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models.

• Scheel, Ida & Hinnerichsen, Mikkel (2012). The Impact of Climate Change on Precipitation-related Insurance Risk: A Study of the Effect of Future Scenarios on Residential Buildings in Norway. Geneva papers on risk and insurance. Issues and practice. ISSN 1018-5895. 37(2), p. 365–376. doi: 10.1057/gpp.2012.7.
• 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 precipitation over the Norwegian mainland. Tellus A: Dynamic Meteorology and Oceanography. ISSN 0280-6495. 63(4), p. 746–756. doi: 10.1111/j.1600-0870.2011.00525.x.
• Scheel, Ida; Green, Peter J. & Rougier, Jonathan C. (2011). A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models. Scandinavian Journal of Statistics. ISSN 0303-6898. 38(3), p. 529–550. doi: 10.1111/j.1467-9469.2010.00717.x.
• Parr, CL; Hjartåker, Anette; Hjartaker, A; Scheel, Ida; Lund, Eiliv & Laake, Petter [Show all 8 contributors for this article] (2008). Comparing methods for handling missing values in food-frequency questionnaires and proposing k nearest neighbours imputation: effects on dietary intake in the Norwegian Women and Cancer study (NOWAC). Public Health Nutrition (PHN). ISSN 1368-9800. 11, p. 361–370. doi: 10.1017/S1368980007000365.
• Parr, Christine Louise; Hjartåker, Anette; Scheel, Ida; Lund, Eiliv; Laake, Petter & Veierød, Marit Bragelien (2007). Comparing methods for handling missing values in food-frequency questionnaires and proposing k nearest neighbours imputation: effects on dietary intake in the Norwegian Women and Cancer study (NOWAC). Public Health Nutrition (PHN). ISSN 1368-9800.
• Scheel, Ida; Aldrin, Magne; Frigessi, Arnoldo & Jansen, Peder A (2007). A stochastic model for infectious salmon anemia (ISA) in Atlantic salmon farming. Journal of the Royal Society Interface. ISSN 1742-5689. 4. doi: 10.1098/rsif.2007.0217. Show summary

  Infectious salmon anemia (ISA) is one of the main infectious diseases in Atlantic salmon farming with major economical implications. Despite the strong regulatory interventions, the ISA epidemic is not under control, worldwide. We study the data covering salmon farming in Norway from 2002 to 2005 and propose a stochastic space-time model for the transmission of the virus. We model seaway transmission between farm sites, transmission through shared management and infrastructure, biomass effects and other potential pathways within the farming industry. We find that biomass has an effect on infectiousness, the local contact network and seaway distance of 5 km represent similar risks, but a large component of risk originates from other sources, among which are possibly infected salmon smolt and boat traffic.

• Scheel, Ida; Aldrin, Magne; Glad, Ingrid Kristine; Sorum, Ragnhild; Lyng, Heidi & Frigessi, Arnoldo (2005). The influence of missing value imputation on detection of differentially expressed genes from microarray data. Bioinformatics. ISSN 1367-4803. 21(23), p. 4272–4279.

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• Aalen, Odd O.; Frigessi, Arnoldo; Moger, Tron Anders; Scheel, Ida; Skovlund, Eva & Veierød, Marit Bragelien (2018). Statistiske metoder i medisin og helsefag. 2. utgave. Gyldendal Akademisk. ISBN 978-82-05-50764-7. 351 p.
• Aalen, Odd O.; Frigessi, Arnoldo; Moger, Tron Anders; Scheel, Ida; Skovlund, Eva & Veierød, Marit Bragelien (2006). Statistiske metoder i medisin og helsefag. Gyldendal Akademisk. ISBN 978-82-05-34685-7. 335 p.

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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.
• Haug, Ola; Frigessi, Arnoldo; Scheel, Ida & Guttorp, Peter (2015). Modelling and predicting residential water damage insurance claims in a climate change perspective.
• 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.
• Scheel, Ida; Haug, Ola; Orskaug, Elisabeth; Frigessi, Arnoldo & Guttorp, Peter (2012). Evaluating and Calibrating Dynamically Downscaled Precipitation Using the Doksum Shift Function.
• Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Maraun, Douglas & Guttorp, Peter (2012). Evaluation and calibration of dynamically downscaled precipitation over Norwegian mainland.
• Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Maraun, Douglas & Guttorp, Peter (2012). Evaluation and Calibration of Dynamically Downscaled Precipitation over Norwegian Mainland.
• Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Guttorp, Peter & Maraun, Douglas (2011). Calibrating dynamically down-scaled precipitation using the Doksum shift function.
• 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.
• Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Guttorp, Peter & Maraun, Douglas (2011). Calibrating dynamically downscaled precipitation using the Doksum shift function.
• Scheel, Ida; Green, Peter J & Rougier, Jonathan C (2011). A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models.
• Scheel, Ida; Green, Peter J & Rougier, Jonathan C (2010). A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models.
• Scheel, Ida; Green, Peter J & Rougier, Jonathan C (2010). A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models.
• Orskaug, Elisabeth; Haug, Ola; Scheel, Ida & Frigessi, Arnoldo (2010). A validation suite for downscaled climate model data.
• Scheel, Ida; Green, Peter J & Rougier, Jonathan C (2009). A graphical diagnostic for identifying influential model choices in Bayesian hierarchical models.
• Scheel, Ida; Green, Peter J & Rougier, Jonathan C (2008). Identifying influential model choices in Bayesian hierarchical models.
• Scheel, Ida; Green, Peter J & Rougier, Jonathan C (2008). Identifying influential model choices in Bayesian hierarchical models.
• Lyngstad, Trude M.; Scheel, Ida & Jansen, Peder A. (2007). Hvordan spres infeksiøs lakseanemi (ILA)? Norsk Fiskeoppdrett. ISSN 0332-7132. 32(9), p. 52–55.
• Scheel, Ida; Aldrin, Magne; Frigessi, Arnoldo & Jansen, Peder A (2007). A stochastic model for infectious salmon anemia (ISA) in Atlantic salmon farming.
• 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.
• 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.

• 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.
• 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.
• 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.

• Scheel, Ida (2004). Spread of infectious agents in salmon farming.
• 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
• 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.
• Scheel, Ida & Hinnerichsen, Mikkel (2010). The Impact of Climate Change on Insurance Risk: A Study of the Effect of Climate Change Scenarios in Norway. Norwegian Computing Center.
• Scheel, Ida; Green, Peter J & Rougier, Jonathan C (2010). Applications of the Local critique plot. Department of Mathematics, University of Oslo. ISSN 0806-3842.
• Scheel, Ida; Green, Peter J & Rougier, Jonathan C (2008). Identifying influential model choices in Bayesian hierarchical models. Department of Mathematics, University of Bristol.
• Scheel, Ida (2008). Influence of incomplete knowledge in inference: cases in the life sciences. Faculty of Mathematics and Natural Sciences, University of Oslo. ISSN 1501-7710.
• Frigessi, Arnoldo & Scheel, Ida (2002). A first statistical methods for the geofraphic localisation of GSM mobile phones.

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