I am employed at
Statistics Norway, Division for Statistical Methods and Standards.
Langsrud, Ø. (20??), Information Preserving Regression-based Tools for Statistical Disclosure Control, Statistics and Computing, ??, ??-??. [Abstract] [pdf]
Langsrud, Ø., Jørgensen, K., Ragni Ofstad, R. and Næs, T. (2007), Analyzing Designed Experiments with Multiple Responses, Journal of Applied Statistics, 34, 1275-1296. [Abstract] [pdf]
Langsrud, Ø. (2006), Explaining Correlations by Plotting Orthogonal Contrasts, The American Statistician, 60, 335-339. [Abstract] [pdf]
Langsrud, Ø. (2005), Rotation Tests, Statistics and Computing, 15, 53-60. [Abstract]
Moen, B., Oust, A., Langsrud, Ø., Dorrell, N., Gemma, L., Marsden, G.L., Hinds, J., Kohler, A., Wren, B.W. and Rudi, K. (2005), An explorative multifactor approach for investigating global survival mechanisms of Campylobacter jejuni under environmental conditions, Applied and Environmental Microbiology, 71, 2086-2094. [Abstract] [ pdf ]
Langsrud, Ø. (2004), The Geometrical Interpretation of Statistical Tests in Multivariate Linear Regression, Statistical Papers, 45, 111-122. [Abstract]
Langsrud, Ø. and Næs, T. (2003), Optimised Score Plot by Principal Components of Predictions, Chemometrics and Intelligent Laboratory Systems, 68, 61-74. [Abstract]
Langsrud, Ø. (2003), ANOVA for Unbalanced Data: Use Type II Instead of Type III Sums of Squares, Statistics and Computing, 13, 163-167. [Abstract]
Langsrud, Ø. (2002), 50-50 Multivariate Analysis of Variance for Collinear Responses, The Statistician, 51, 305-317. [Abstract]
Langsrud, Ø. (2001), Identifying Significant Effects in Fractional Factorial Multiresponse Experiments, Technometrics, 43, 415-424. [Abstract]
Moen B., Janbu A.O., Langsrud S., Langsrud Ø., Hobman J., Constantinidou C., Kohler A., and Rudi K. (IN PRESS), Global responses of Escherichia coli to adverse conditions determined by microarrays and FT-IR spectroscopy, Canadian Journal of Microbiology.
Bahuaud D., Mørkøre T., Langsrud Ø., Sinnes K., Veiseth E., Ofstad R., Thomassen M.S. (2008), Effects of -1.5 °C Super-chilling on Quality of Atlantic Salmon (Salmo salar) Pre-Rigor Fillets: Cathepsin activity, Muscle Histology, Texture and Liquid Leakage, Food Chemistry. 111, 329-339.
Bjerke, F., Langsrud, Ø., Aastveit, A.H. (2008), Restricted randomisation and multiple responses in industrial experiments, Quality and Reliability Engineering International. 24, 167-181.
Hollung, K., Veiseth, E., Frøystein, T., Aass, L., Langsrud, Ø., Hildrum, K.I. (2007), Variation in the response to manipulation of post mortem glycolysis in beef muscles by low-voltage electrical stimulation and conditioning temperature, Meat Science. 77, 372-383.
Nordvi, B, Langsrud, Ø., Egelandsdal, B., Slinde, E., Vogt, G., Gutierrez, M., Olsen, E. (2007), Characterization of Volatile Compounds in a Fermented and Dried Fish Product during Cold Storage, Journal of Food Science. 72, S373-S380.
Bore, E., Langsrud, S., Langsrud, Ø., Rode, T.M., Holck, A. (2007), Acid shock responses in staphylococcus aureus investigated by global gene expression analysis, Microbiology, 153, 2289-2303.
Nordvi, B, Egelandsdal, B., Langsrud, Ø., Ofstad, R., Slinde, E. (2007), Development of a novel, fermented and dried saithe and salmon product, Innovative Food Science & Emerging Technologies, 8, 163-171.
Bore, E., Hebraud, M., Chafsey, I., Chambon, C., Skjæret, C., Moen, B., Møretrø, T., Langsrud, Ø., Rudi, K., Langsrud, S. (2007), Adapted tolerance to benzalkonium chloride in Escherichia coli K-12 studied by transcriptome and proteome analyses, Microbiology, 153, 935-946.
Sørheim, O., Langsrud, Ø., Cornforth, D.P., Johannessen, T.C., Slinde, E., Berg, P., Nesbakken, T. (2006), Carbon monoxide as a colorant in cooked or fermented sausages, Journal of Food Science, 71, C549-C555.
Egelandsdal, B., Dingstad, G.I., Tøgersen, G., Lundby, F., Langsrud, Ø. (2005), Autofluorescence quantifies collagen in sausage batters with a large variation in myoglobin content, Meat Science, 69, 35-46.
Ofstad, R., Langsrud, Ø., Nyvold, T.E., Enersen, G., Høst, V., Willers, E.P., Nordvi, B., Egelandsdal, B. (2005), Heat processed whey-protein food emulsions and growth of shear-induced cracks during cooling, LWT - Food Science and Technology, 38, 29-39.
Kihlberg, I., Johansson, L., Langsrud, Ø., Risvik, E. (2005), Effects of information on liking of bread, Food Quality and Preference, 16, 25-35.
Egelandsdal, B., Langsrud, Ø., Nyvold, T.E., Sontum, P.K., Sørensen, C., Enersen, G., Hølland, S. and Ofstad, R. (2001), Estimating significant causes of variation in emulsions’ droplet size distributions obtained by the electrical sensing zone and laser low angle light scattering techniques, Food Hydrocolloids, 15, 521-532.
Langsrud, Ø. and Næs, T. (1998), A Unified Framework for Significance Testing in Fractional Factorials, Computational Statistics and Data Analysis, 28, 413-431.
Næs, T. and Langsrud, Ø. (1998), Fixed or random assessors in sensory profiling?, Food Quality and Preference, 9, 145-152.
Langsrud, Ø. and Næs, T. (1995), On the Structure of PLS in Orthogonal Designs, Journal of Chemometrics, 9, 483-487.
Langsrud, Ø., Næs, T. and Ellekjær, M. R. (1994), Identifying Significant Effects in Fractional Factorial Designs, Journal of Chemometrics, 8, 205-219.
This paper presents a unified framework for regression-based statistical disclosure control for microdata. A basic method, known as information preserving statistical obfuscation (IPSO), produces synthetic data that preserve variances, covariances and fitted values. The data are then generated conditionally according to the multivariate normal distribution. Generalizations of the IPSO method are described in the literature and these methods aim to generate data more similar to the original data. This paper describes these methods in a concise and interpretable way, which is close to efficient implementation. Decomposing the residual data into orthogonal scores and corresponding loadings is an essential part of the framework. Both QR decomposition (Gram Schmidt orthogonalization) and singular value decomposition (principal components) may be used. Within this framework, new and generalized methods are presented. In particular, a method is described by means of which the correlations to the original principal component scores can be controlled exactly. It is shown that a suggested method of random orthogonal matrix masking (ROMM) can be implemented without generating an orthogonal matrix. Generalized methodology for hierarchical categories is presented within the context of microaggregation. Some information can then be preserved at the lowest level and more information at higher levels. The presented methodology is also applicable to tabular data. One possibility is to replace the content of primary and secondary suppressed cells with generated values. It is proposed replacing suppressed cell frequencies with decimal numbers and it is argued that this can be a useful method.
KEY WORDS: microdata anonymization, synthetic data, microaggregation, hybrid microdata, cell suppression.
KEY WORDS: 50-50 MANOVA, General linear model, Least-squares means, Multiple testing, Principal component, Rotation test, Unbalanced factorial design.
KEY WORDS: Design of experiments, Fractional factorial design, Scatterplot, General linear model, Partial least squares, Principal component analysis.
This paper describes a generalised framework for doing Monte Carlo tests in multivariate linear regression. The rotation methodology assumes multivariate normality and is a true generalisation of the classical multivariate tests - any imaginable test statistic is allowed. The generalised test statistics are dependent on the unknown covariance matrix. Rotation testing handles this problem by conditioning on sufficient statistics.
Compared to permutation tests, we replace permutations by proper random rotations. Permutation tests avoid the multinormal assumption, but they are limited to relatively simple models. On the other hand, a rotation test can, in particular, be applied to any multivariate generalisation of the univariate F-test.
As an important application, a detailed description of how each single response p-value can be non-conservatively adjusted for multiplicity is given. This method is exact and non-conservative (unlike Bonferroni), and it is a generalisation of the ordinary F-test (except for the computation by simulations). Hence, this paper offers an exact Monte Carlo solution to a classical problem of multiple testing.
KEY WORDS: Conditional inference, Multiple testing, Random orthogonal matrix, Adjusted p-value, Multiple endpoints, Spherical distribution, Microarray data analysis.
KEY WORDS: Campylobacter jejuni, survival in the environment, microarray, FT-IR spectroscopy, 50-50 MANOVA, False Discovery Rate.
KEY WORDS: Multivariate analysis, Wilks' lambda distribution, MANOVA, Canonical correlation, Random rotation, Invariance.
KEY WORDS: PLS, PCR, principal components, scores plot, loading plot, reduced-rank regression.
F. Yates (1934)
The Analysis of Multiple Classifications With Unequal Numbers in the Different Classes,
Journal of the American Statistical Association ,
29, 51-66 .
2) J. A. Nelder and P. W. Lane (1995) The Computer Analysis of Factorial Experiments: In Memoriam - Frank Yates, The American Statistician 49, 382-385.
KEY WORDS: Unbalanced factorial design, Linear model, Fixed effect, Nonorthogonal, Fitting constants, Constraint.
KEY WORDS: Multiresponse, Significance testing, Principal component, Hotelling's T 2, Experimental design, Stabilized multivariate tests.
KEY WORDS: Active factors, Experimental design, Monte Carlo testing, Multiple response, Subset selection, Unreplicated fractional factorials.