
Øyvind Langsrud
Oyvind.Langsrudssb.no

I am employed at
Statistics Norway,
Division for Statistical Methods and Standards.
PUBLICATIONS
Selected Journal Papers
Langsrud, Ø. (2019),
Information Preserving Regressionbased Tools for Statistical Disclosure Control,
Statistics and Computing,
29, 965976.
[Abstract]
[pdf]
Langsrud, Ø.,
Jørgensen, K.,
Ragni Ofstad, R.
and Næs, T. (2007),
Analyzing Designed Experiments with Multiple Responses,
Journal of Applied Statistics,
34, 12751296.
[Abstract]
[pdf]
Langsrud, Ø. (2006),
Explaining Correlations by Plotting Orthogonal Contrasts,
The American Statistician,
60, 335339.
[Abstract]
[pdf]
Langsrud, Ø. (2005),
Rotation Tests,
Statistics and Computing,
15, 5360.
[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, 20862094.
[Abstract]
[ pdf ]
Langsrud, Ø. (2004),
The Geometrical Interpretation of Statistical Tests in Multivariate Linear Regression,
Statistical Papers,
45, 111122.
[Abstract]
Langsrud, Ø. and
Næs, T.
(2003),
Optimised Score Plot by Principal Components of Predictions,
Chemometrics and Intelligent Laboratory Systems,
68, 6174.
[Abstract]
Langsrud, Ø. (2003),
ANOVA for Unbalanced Data: Use Type II Instead of Type III Sums of Squares,
Statistics and Computing,
13, 163167.
[Abstract]
Langsrud, Ø.
(2002),
5050 Multivariate Analysis of Variance for Collinear Responses,
The Statistician,
51, 305317.
[Abstract]
Langsrud, Ø.
(2001),
Identifying Significant Effects in Fractional Factorial Multiresponse Experiments,
Technometrics,
43, 415424.
[Abstract]
Other Journal Papers  See ResearchGate
for a more updated version.
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 FTIR 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 Superchilling on Quality of Atlantic Salmon (Salmo salar) PreRigor
Fillets: Cathepsin activity, Muscle Histology, Texture and Liquid Leakage,
Food Chemistry.
111, 329339.
Bjerke, F.,
Langsrud, Ø.,
Aastveit, A.H.
(2008),
Restricted randomisation and multiple responses in industrial experiments,
Quality and Reliability Engineering International.
24, 167181.
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
lowvoltage electrical stimulation and conditioning temperature,
Meat Science.
77, 372383.
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, S373S380.
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, 22892303.
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, 163171.
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 K12 studied by transcriptome and proteome analyses,
Microbiology,
153, 935946.
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, C549C555.
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, 3546.
Ofstad, R.,
Langsrud, Ø.,
Nyvold, T.E.,
Enersen, G.,
Høst, V.,
Willers, E.P.,
Nordvi, B.,
Egelandsdal, B.
(2005),
Heat processed wheyprotein food emulsions and growth of shearinduced cracks during cooling,
LWT  Food Science and Technology,
38, 2939.
Kihlberg, I.,
Johansson, L.,
Langsrud, Ø.,
Risvik, E.
(2005),
Effects of information on liking of bread,
Food Quality and Preference,
16, 2535.
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, 521532.
Langsrud, Ø. and
Næs, T.
(1998),
A Unified Framework for Significance Testing in Fractional Factorials,
Computational Statistics and Data Analysis,
28, 413431.
Næs, T. and
Langsrud, Ø.
(1998),
Fixed or random assessors in sensory profiling?,
Food Quality and Preference,
9, 145152.
Langsrud, Ø. and
Næs, T.
(1995),
On the Structure of PLS in Orthogonal Designs,
Journal of Chemometrics, 9, 483487.
Langsrud, Ø.,
Næs, T. and
Ellekjær, M. R.
(1994),
Identifying Significant Effects in Fractional Factorial Designs,
Journal of Chemometrics, 8, 205219.
Some Conference Proceedings
 Langsrud, Ø. (2024)
 Secondary Cell Suppression by Gaussian Elimination: An Algorithm Suitable for Handling Issues with Zeros and Singletons,
Will be published in the Springer LNCS proceedings of the conference Privacy in Statistical Databases.
[ pdf ]
 Langsrud, Ø. (2005)
 Adjusted pvalues by rotation testing,
MCP 2005, The 4th international conference on Multiple Comparison Procedures,
(SLIDES ONLY),
The slides can be found at
the conference website.
 Langsrud, Ø., Jørgensen, K. and Haugdal, J. (2003)
 Tools for analysing designed multiresponse experiments,
Proceedings of the Third Annual meeting of ENBIS and ISIS3,
(SLIDES ONLY),
The proceedings were published on a CDRom.
See the ENBIS website.
[ pdf ]
 Langsrud, Ø. and
Næs, T.
(2001)

Optimised score plot by principal components of predictions,
Proceedings of the 2nd International Symposium of PLS and Related Methods
,
(Eds. Vinzi, Lauro, Morineau, Tenenhaus) CisiaCeresta Montreuil, France,
ISBN 2906711489.
[ pdf ]
 Langsrud, Ø., Egelandsdal, B., and Ofstad, R. (2001)
 Analysing Designed Experiments with Multiple Responses,
Proceedings of the First Annual ENBIS Conference,
(SLIDES ONLY),
The proceedings were published on a CDRom.
See the ENBIS website.
[ pdf ]
 Langsrud Ø. (2000)

FiftyFifty MANOVA:
Multivariate Analysis of Variance for Collinear Responses,
Proceedings of The Industrial Statistics in Action 2000,
vol. 2, p. 250264, University of Newcastle upon Tyne.
Other
 Langsrud, Ø.
(1997)

Identifying Significant Effects in
Fractional Factorial Single and
Multiresponse Experiments
Doctor Scientarium Thesis,
Agricultural University of Norway.
 Langsrud, Ø. and
Næs, T.
(2002) [in Norwegian]
 PCP: Nytt og Optimalisert Plott av Skårer og Ladninger.
Poster presentert på Det 14. Norske Kjemometrisymposium, Gol.
pdf
 Also see:

Publications at Norwegian Computing Center
( NR )
Abstracts of Selected Journal Papers
Langsrud, Ø. (20??),
Information Preserving Regressionbased Tools for Statistical Disclosure Control,
Statistics and Computing,
??, ????.
[pdf]
ABSTRACT
This paper presents a unified framework for regressionbased 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.
Langsrud, Ø.,
Jørgensen, K.,
Ragni Ofstad, R.
and Næs, T. (2007),
Analyzing Designed Experiments with Multiple Responses,
Journal of Applied Statistics,
34, 12751296.
[pdf]
ABSTRACT:
This paper is an overview of a unified framework for analyzing designed experiments with univariate or multivariate responses. Both categorical and continuous design variables are considered. To handle unbalanced data, we introduce the socalled Type II* sums of squares. This means that the results are independent of the scale chosen for continuous design variables. Furthermore, it does not matter whether twolevel variables are coded as categorical or continuous.
Overall testing of all responses is done by 5050 MANOVA, which handles several highly correlated responses. Univariate pvalues for each response are adjusted by using rotation testing. To illustrate multivariate effects, mean values and mean predictions are illustrated in a principal component score plot or directly as curves. For the unbalanced cases, we introduce a new variant of adjusted means, which are independent to the coding of twolevel variables.
The methodology is exemplified by case studies from cheese and fish pudding production.
KEY WORDS:
5050 MANOVA,
General linear model,
Leastsquares means,
Multiple testing,
Principal component,
Rotation test,
Unbalanced factorial design.
Langsrud, Ø. (2006),
Explaining Correlations by Plotting Orthogonal Contrasts,
The American Statistician,
60, 335339.
[pdf]
ABSTRACT
(
Copyright ©
2006 American Statistical Association
)
:
This article describes a new plot that aids understanding the relationship between two response variables in a designed
experiment. In addition to plotting the observed values directly, we make a scatter
plot of orthogonal contrasts from the general linear model. This plot contains the same correlation
information as the ordinary scatter plot. Therefore, one can interpret how the effects of the various
design variables contribute to the correlation coefficient. This idea is also useful in more general
cases. Any graphic presentation of the original observations can be accompanied by a corresponding plot
of orthogonal contrasts that often will clarify the interpretation.
KEY WORDS:
Design of experiments,
Fractional factorial design,
Scatterplot,
General linear model,
Partial least squares,
Principal component analysis.
Langsrud, Ø. (2005),
Rotation Tests,
Statistics and Computing,
15, 5360.
ABSTRACT
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
Ftest.
As an important application, a detailed description of how each single response pvalue can be
nonconservatively adjusted for multiplicity is given. This method is exact and nonconservative
(unlike Bonferroni), and it is a generalisation of the ordinary Ftest (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 pvalue,
Multiple endpoints,
Spherical distribution,
Microarray data analysis.
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, 20862094.
[ pdf ]
ABSTRACT
(
Copyright ©
American Society for Microbiology
)
:
Explorative approaches such as DNA microarray experiments are becoming increasingly important in
microbial research. Despite these major technical advancements,
approaches to study multifactor experiments are still lacking. We have addressed this problem
using rotation testing and a novel MANOVA approach (5050 MANOVA) to investigate interacting
experimental factors in a complex experimental design. Furthermore, a new rotation testing
based method was introduced to calculate false discovery rates for each response. This novel
analytical concept was used to investigate global survival mechanisms in the environment of
the major food borne pathogen C. jejuni. We simulated nongrowth environmental conditions
by investigating combinations of the factors temperature (5 and 25°
C) and oxygen tension (anaerobic, microaerobic and aerobic). Data were generated using DNA microarrays
for information about gene expression patterns coupled with FTIR spectroscopy to study global macromolecular
changes in the cell. Microarray analyses showed that most genes are either unchanged or down regulated compared
to the reference (day 0) for the conditions tested, and that the 25°
C anaerobic condition gave the most distinct expression pattern with the fewest genes expressed.
The few up regulated genes are generally stress related and/or related to the cell envelope.
We found, using FTIR spectroscopy, that the amount of polysaccharides/oligosaccharides increases
under the nongrowth survival conditions. Potential mechanisms for survival could be to down
regulate most functions to save energy, and produce polysaccharides/oligocaccharides for protection
against harsh environments. Basic knowledge about the survival mechanisms is of fundamental importance
in preventing transmission of this bacterium through the food chain.
KEY WORDS:
Campylobacter jejuni,
survival in the environment,
microarray,
FTIR spectroscopy,
5050 MANOVA,
False Discovery Rate.
Langsrud, Ø. (2004),
The Geometrical Interpretation of Statistical Tests in Multivariate Linear Regression,
Statistical Papers,
45, 111122.
ABSTRACT:
A geometrical interpretation of the classical tests of the relation between two sets of
variables is presented. One of the variable sets may be considered as fixed and then we
have a multivariate regression model. When the Wilks' lambda distribution is viewed
geometrically it is obvious that the two special cases, the F distribution and the
Hotelling T ^{2}
distribution are equivalent. From the geometrical perspective it is
also obvious that the test statistic and the pvalue are unchanged if the responses
and the predictors are interchanged.
KEY WORDS:
Multivariate analysis,
Wilks' lambda distribution,
MANOVA,
Canonical correlation,
Random rotation,
Invariance.
Langsrud, Ø. and
Næs, T.
(2003),
Optimised Score Plot by Principal Components of Predictions,
Chemometrics and Intelligent Laboratory Systems,
68, 6174.
ABSTRACT:
A common problem in statistics/chemometrics is to relate two data matrices
(X and Y) to each other, with the purpose of either prediction or
interpretation. Usually one is interested in understanding which
directions in Yspace that can be predicted by which directions
in Xspace. Several methods exist for this, for instance PLS
regression and canonical correlation.
The present paper presents a new plot for
visualising the relationship between X and Y.
The plot is based on a decomposition of the Xspace that
is optimal with respect to Yvariance.
The new procedure can accompany any regression method.
KEY WORDS:
PLS,
PCR,
principal components,
scores plot,
loading plot,
reducedrank regression.
Langsrud, Ø. (2003),
ANOVA for Unbalanced Data: Use Type II
Instead of Type III Sums of Squares,
Statistics and Computing,
13, 163167.
ABSTRACT:
Methods for analyzing unbalanced factorial designs can be traced back to Yates in
1934 ^{1)}. Today, most major statistical programs perform, by default,
unbalanced ANOVA based on Type III sums of squares (Yates's weighted squares of means).
As criticized by Nelder and Lane ^{2)}, this analysis is founded on unrealistic
models  models with interactions, but without all corresponding main effects.
The Type II analysis (Yates's method of fitting constants) is usually not preferred
because of the underlying assumption of no interactions. This argument is, however,
also founded on unrealistic models. Furthermore, by considering the power of the two
methods, it is clear that Type II is preferable.
1)
F. Yates (1934)
The Analysis of Multiple Classifications With Unequal Numbers in the Different Classes,
Journal of the American Statistical Association ,
29, 5166 .
2)
J. A. Nelder and P. W. Lane (1995)
The Computer Analysis of Factorial Experiments: In Memoriam  Frank Yates,
The American Statistician
49, 382385.
KEY WORDS:
Unbalanced factorial design,
Linear model,
Fixed effect,
Nonorthogonal,
Fitting constants,
Constraint.
Langsrud, Ø. (2002),
5050 Multivariate Analysis of Variance for Collinear Responses,
The Statistician,
51, 305317.
ABSTRACT:
Classical multivariate analysisofvariance
tests perform poorly in cases with several highly correlated
responses and the tests collapse when the number of responses exceeds the
number of observations. This paper presents a new method which handles this
problem. The dimensionality of the data is reduced by using principal component
decompositions and the final tests are still based on the classical test statistics and
their distributions.
The methodology is illustrated with an example from the production of sausages with
responses from near infrared reflectance spectroscopy. A closely related method for
testing relationships in uniresponse regression with collinear explanatory variables is
also presented. The new test,
which is called the 5050 Ftest, uses the first k components to calculate
SS_{MODEL}.
The next d components are not involved in
SS_{ERROR}
and they are called buffer components.
KEY WORDS:
Multiresponse,
Significance testing,
Principal component,
Hotelling's T ^{2},
Experimental design,
Stabilized multivariate tests.
Langsrud, Ø. (2001),
Identifying Significant Effects in Fractional Factorial Multiresponse Experiments,
Technometrics ,
43, 415424.
ABSTRACT:
A forward selection procedure for identifying active contrasts in
unreplicated factorial multiresponse experiments is presented. To
test the multivariate effects, pvalues are calculated by Monte
Carlo sampling; the ordinary Hotelling's T ^{2} test is
a special case. Collinear responses are effectively handled by
successive principal component decompositions, as when, for
instance, the number of response variables exceeds the number
of observations. In the univariate case, the test statistic
pools the smallest effects as error. Independent sources for
error degrees of freedom can be incorporated. The method is
illustrated with examples from baguette and mayonnaise production.
KEY WORDS:
Active factors,
Experimental design,
Monte Carlo testing,
Multiple response,
Subset selection,
Unreplicated fractional factorials.