Casper Albers | University of Groningen (original) (raw)

Papers by Casper Albers

Purpose – Despite their growing popularity among organisations, satisfaction with activity-based ... more Purpose – Despite their growing popularity among organisations, satisfaction with activity-based work (ABW) environments is found to be below expectations. Research also suggests that workers typically do not switch frequently, or not at all, between different activity settings. Hence, the purpose of this study is to answer two main questions: Is switching behaviour related to satisfaction with ABW environments? Which factors may explain switching behaviour? Design/methodology/approach – Questionnaire data provided by users of ABW environments (n 3,189) were used to carry out ANOVA and logistic regression analyses. Findings – Satisfaction ratings of the 4 per cent of the respondents who switched several times a day appeared to be significantly above average. Switching frequency was found to be positively related to heterogeneity of the activity profile, share of communication work and external mobility. Practical implications – Our findings suggest that satisfaction with ABW environments might be enhanced by stimulating workers to switch more frequently. However, as strong objections against switching were observed and switching frequently does not seem to be compatible with all work patterns, this will presumably not work for everyone. Many workers are likely to be more satisfied if provided with an assigned (multifunctional) workstation. Originality/value – In a large representative sample, clear evidence was found for relationships between behavioural aspects and appreciation of ABW environments that had not been studied previously.

Brazilian Journal of Probability and Statistics, 2016

Proceedings of the National Academy of Sciences, 2015

Congestion on roads is a major problem worldwide. Many roads now have induction loops implanted i... more Congestion on roads is a major problem worldwide. Many roads now have induction loops implanted into the road surface providing real-time traffic flow data. These data can be used in a traffic management system to monitor current traffic flows in a network so that traffic can be directed and managed efficiently. Reliable short-term forecasting and monitoring models of traffic flows are crucial for the success of any traffic management system.

PLOS ONE, 2015

The introduction of computer-based testing in high-stakes examining in higher education is develo... more The introduction of computer-based testing in high-stakes examining in higher education is developing rather slowly due to institutional barriers (the need of extra facilities, ensuring test security) and teacher and student acceptance. From the existing literature it is unclear whether computer-based exams will result in similar results as paper-based exams and whether student acceptance can change as a result of administering computer-based exams. In this study, we compared results from a computer-based and paper-based exam in a sample of psychology students and found no differences in total scores across the two modes. Furthermore, we investigated student acceptance and change in acceptance of computer-based examining. After taking the computer-based exam, fifty percent of the students preferred paper-and-pencil exams over computer-based exams and about a quarter preferred a computer-based exam. We conclude that computer-based exam total scores are similar as paper-based exam scores, but that for the acceptance of high-stakes computer-based exams it is important that students practice and get familiar with this new mode of test administration.

Journal of the Royal Statistical Society Series C Applied Statistics, Mar 1, 2013

Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a mult... more Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting of traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending on the time of day and accommodating measurement errors occurring due to data collection errors. This paper extends LMDMs to address these issues. Additionally, the paper investigates how close the approximate forecast limits usually used with the LMDM are to the true, but not so readily available, forecast limits.

The use of intervention for time series modelling is a well established technique for on-line for... more The use of intervention for time series modelling is a well established technique for on-line forecasting and decision-making in the context of Bayesian dynamic linear models. Intervention has also been recently used in (non-dynamic) Bayesian networks to investigate causal relationships between variables, and in dynamic Bayesian networks to investigate lagged causal relationships between time series. The Multiregression Dynamic Model (MDM) is a Bayesian dynamic model and an example of a dynamic Bayesian network. The focus of this paper is the use of intervention in the MDM. It will be demonstrated that not only is intervention in the MDM a powerful tool for forecasting, but intervention can also aid in the identification of contemporaneous causal relationships between time series, thus going beyond the identification of lagged causal relationships previously addressed in dynamic Bayesian networks. 1 The multiregression dynamic model (MDM) (Queen and Smith, 1993) is an example of a DBN. The MDM is a Bayesian dynamic model and is defined to preserve any conditional independences, related to causality, across a multivariate time series over time. At each time t, the observable component series Y t (1), . . . , Y t (n) of the n-dimensional time series, and their associated state vectors θ t (1), . . . , θ t (n), are represented by a BN. These individual BNs are linked together over time to form a DBN. The MDM then uses the conditional independences and causal driving mechanism through the system, as represented by the DBN, to break down the multivariate model into simpler univariate components. There are many potential application areas for the MDM, including problems in economics (modelling various economic indicators such as energy consumption and GDP), environmental problems (such as water and other resource management problems), industrial problems (such as product distribution flow problems) and medical problems (such as patient physiological monitoring). Queen (1994) and Queen et al. (2007b) use an MDM to model monthly brand sales in a competitive market. Here, the competition in the market is the causal drive within the system and is used to define a conditional independence structure across the time series. Queen (1997) and Queen et al. (1994) focus on how the DBN at time t may be elicited for MDMs for modelling markets. Following Whitlock and Queen and , this paper considers (in Section 4) the specific application of forecasting traffic flows at various points in a road network. In this application, the direction of traffic flow produces the causal drive through the system and the possible routes through the system are used to define a conditional independence structure across the time series.

Journal of Multivariate Analysis, 2011

a b s t r a c t showed that the problem min

Computers & Education, 2015

Summary In canonical analysis with more variables than samples, it is shown that, as well as the ... more Summary In canonical analysis with more variables than samples, it is shown that, as well as the usual canonical means in the range-space of the within-groups dispersion matrix, canonical means may be dened in its null space. In the range space we have the usual

Journal of Multivariate Analysis, 2014

ABSTRACT Visualisations of two-way arrays are well-understood. Here, a procedure, with geometric ... more ABSTRACT Visualisations of two-way arrays are well-understood. Here, a procedure, with geometric underpinning, is given for visualising rank-two three-way arrays in two-dimensions.

Studies in Classification, Data Analysis, and Knowledge Organization, 2007

The set of Euclidean distance matrices has a well-known representation as a convex cone. The prob... more The set of Euclidean distance matrices has a well-known representation as a convex cone. The problems of representing the group averages of K distance matrices are discussed, but not fully resolved, in the context of SMACOF, Generalized Orthogonal Procrustes Analysis and Individual Differences Scaling. The polar (or dual) cone representation, corresponding to inner-products around a centroid, is also discussed. Some new characterisations of distance cones in terms of circumhyperspheres are presented.

ABSTRACT . In the article `How To Assign Probabilities if you must'[1] several methods to... more ABSTRACT . In the article `How To Assign Probabilities if you must'[1] several methods to assign probabilities applied to a two-played die rolling game were discussed. The main focus was on methods using the logarithmic loss function for the choice of proper loss function. In this additional part, we will discuss the Brier and Epstein loss functions as an alternative. Futhermore, an extensive graphical display of the situation will be made. For notation and terminology, the reader is referred to Albers et al.[1] 1. Extension to other proper loss functions We consider three proper loss functions: logarithmic (L log ), Brier (LB ) and Epstein (LE ) loss L log (y; Q(x)) = Gamma log q x (y) (1) LB (y; Q(x)) = (1 Gamma q x (y)) 2 + X j 6=y (q x (j)) 2 (2) LE (y; Q(x)) = 6 X j=1 / 1 fy;::: ;6g (j) Gamma j X =1 q x () ! 2 (3) For the seven possible combinations of x and y, the losses incorporated are: x y logarithmic Brier Epstein 1 1 log(2) 0:5 1 1 5 log(2) 0:5 1 2 2 Gamma lo...

Statistical inference is about using statistical data (x) to formulate an opinion about something... more Statistical inference is about using statistical data (x) to formulate an opinion about something that is dened well, but unknown ( y). Testing a hypothesis H about y is one of the possibilities, the estimation or prediction ofy is another one. We concentrate the attention on estimation or prediction in the sense that an opinion is required in the form

Open University Statistics Group …, 2008

The use of intervention for time series modelling is a well established technique for on-line for... more The use of intervention for time series modelling is a well established technique for on-line forecasting and decision-making in the context of Bayesian dynamic linear models. Intervention has also been recently used in (non-dynamic) Bayesian networks to investigate causal relationships between variables, and in dynamic Bayesian networks to investigate lagged causal relationships between time series. The Multiregression Dynamic Model (MDM) is a Bayesian dynamic model and an example of a dynamic Bayesian network. The focus of this paper is the use of intervention in the MDM. It will be demonstrated that not only is intervention in the MDM a powerful tool for forecasting, but intervention can also aid in the identification of contemporaneous causal relationships between time series, thus going beyond the identification of lagged causal relationships previously addressed in dynamic Bayesian networks. 1 The multiregression dynamic model (MDM) (Queen and Smith, 1993) is an example of a DBN. The MDM is a Bayesian dynamic model and is defined to preserve any conditional independences, related to causality, across a multivariate time series over time. At each time t, the observable component series Y t (1), . . . , Y t (n) of the n-dimensional time series, and their associated state vectors θ t (1), . . . , θ t (n), are represented by a BN. These individual BNs are linked together over time to form a DBN. The MDM then uses the conditional independences and causal driving mechanism through the system, as represented by the DBN, to break down the multivariate model into simpler univariate components. There are many potential application areas for the MDM, including problems in economics (modelling various economic indicators such as energy consumption and GDP), environmental problems (such as water and other resource management problems), industrial problems (such as product distribution flow problems) and medical problems (such as patient physiological monitoring). Queen (1994) and Queen et al. (2007b) use an MDM to model monthly brand sales in a competitive market. Here, the competition in the market is the causal drive within the system and is used to define a conditional independence structure across the time series. Queen (1997) and Queen et al. (1994) focus on how the DBN at time t may be elicited for MDMs for modelling markets. Following Whitlock and Queen and , this paper considers (in Section 4) the specific application of forecasting traffic flows at various points in a road network. In this application, the direction of traffic flow produces the causal drive through the system and the possible routes through the system are used to define a conditional independence structure across the time series.

Synthese, 2005

After explaining the well-known two-envelope 'paradox' by indicating the fallacy involved, we con... more After explaining the well-known two-envelope 'paradox' by indicating the fallacy involved, we consider the two-envelope 'problem' of evaluating the 'factual' information provided to us in the form of the value contained by the envelope chosen first. We try to provide a synthesis of contributions from economy, psychology, logic, probability theory (in the form of Bayesian statistics), mathematical statistics (in the form of a decision-theoretic approach) and game theory. We conclude that the two-envelope problem does not allow a satisfactory solution. An interpretation is made for statistical science at large. Synthese (2005) 145: 89-109

Statistics & Decisions, 2000

To test the hypothesis H0 : f = ψ that an unknown density f is equal to a specified one, ψ, an es... more To test the hypothesis H0 : f = ψ that an unknown density f is equal to a specified one, ψ, an estimatef of f is compared with ψ. The total variation distance ||f − ψ|| 1 is used as test statistic.

Statistica Neerlandica, 2001

Journal of the Royal Statistical Society: Series C (Applied Statistics), 2013

Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a mult... more Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting of traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending on the time of day and accommodating measurement errors occurring due to data collection errors. This paper extends LMDMs to address these issues. Additionally, the paper investigates how close the approximate forecast limits usually used with the LMDM are to the true, but not so readily available, forecast limits.

Purpose – Despite their growing popularity among organisations, satisfaction with activity-based ... more Purpose – Despite their growing popularity among organisations, satisfaction with activity-based work (ABW) environments is found to be below expectations. Research also suggests that workers typically do not switch frequently, or not at all, between different activity settings. Hence, the purpose of this study is to answer two main questions: Is switching behaviour related to satisfaction with ABW environments? Which factors may explain switching behaviour? Design/methodology/approach – Questionnaire data provided by users of ABW environments (n 3,189) were used to carry out ANOVA and logistic regression analyses. Findings – Satisfaction ratings of the 4 per cent of the respondents who switched several times a day appeared to be significantly above average. Switching frequency was found to be positively related to heterogeneity of the activity profile, share of communication work and external mobility. Practical implications – Our findings suggest that satisfaction with ABW environments might be enhanced by stimulating workers to switch more frequently. However, as strong objections against switching were observed and switching frequently does not seem to be compatible with all work patterns, this will presumably not work for everyone. Many workers are likely to be more satisfied if provided with an assigned (multifunctional) workstation. Originality/value – In a large representative sample, clear evidence was found for relationships between behavioural aspects and appreciation of ABW environments that had not been studied previously.

Brazilian Journal of Probability and Statistics, 2016

Proceedings of the National Academy of Sciences, 2015

Congestion on roads is a major problem worldwide. Many roads now have induction loops implanted i... more Congestion on roads is a major problem worldwide. Many roads now have induction loops implanted into the road surface providing real-time traffic flow data. These data can be used in a traffic management system to monitor current traffic flows in a network so that traffic can be directed and managed efficiently. Reliable short-term forecasting and monitoring models of traffic flows are crucial for the success of any traffic management system.

PLOS ONE, 2015

The introduction of computer-based testing in high-stakes examining in higher education is develo... more The introduction of computer-based testing in high-stakes examining in higher education is developing rather slowly due to institutional barriers (the need of extra facilities, ensuring test security) and teacher and student acceptance. From the existing literature it is unclear whether computer-based exams will result in similar results as paper-based exams and whether student acceptance can change as a result of administering computer-based exams. In this study, we compared results from a computer-based and paper-based exam in a sample of psychology students and found no differences in total scores across the two modes. Furthermore, we investigated student acceptance and change in acceptance of computer-based examining. After taking the computer-based exam, fifty percent of the students preferred paper-and-pencil exams over computer-based exams and about a quarter preferred a computer-based exam. We conclude that computer-based exam total scores are similar as paper-based exam scores, but that for the acceptance of high-stakes computer-based exams it is important that students practice and get familiar with this new mode of test administration.

Journal of the Royal Statistical Society Series C Applied Statistics, Mar 1, 2013

Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a mult... more Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting of traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending on the time of day and accommodating measurement errors occurring due to data collection errors. This paper extends LMDMs to address these issues. Additionally, the paper investigates how close the approximate forecast limits usually used with the LMDM are to the true, but not so readily available, forecast limits.

The use of intervention for time series modelling is a well established technique for on-line for... more The use of intervention for time series modelling is a well established technique for on-line forecasting and decision-making in the context of Bayesian dynamic linear models. Intervention has also been recently used in (non-dynamic) Bayesian networks to investigate causal relationships between variables, and in dynamic Bayesian networks to investigate lagged causal relationships between time series. The Multiregression Dynamic Model (MDM) is a Bayesian dynamic model and an example of a dynamic Bayesian network. The focus of this paper is the use of intervention in the MDM. It will be demonstrated that not only is intervention in the MDM a powerful tool for forecasting, but intervention can also aid in the identification of contemporaneous causal relationships between time series, thus going beyond the identification of lagged causal relationships previously addressed in dynamic Bayesian networks. 1 The multiregression dynamic model (MDM) (Queen and Smith, 1993) is an example of a DBN. The MDM is a Bayesian dynamic model and is defined to preserve any conditional independences, related to causality, across a multivariate time series over time. At each time t, the observable component series Y t (1), . . . , Y t (n) of the n-dimensional time series, and their associated state vectors θ t (1), . . . , θ t (n), are represented by a BN. These individual BNs are linked together over time to form a DBN. The MDM then uses the conditional independences and causal driving mechanism through the system, as represented by the DBN, to break down the multivariate model into simpler univariate components. There are many potential application areas for the MDM, including problems in economics (modelling various economic indicators such as energy consumption and GDP), environmental problems (such as water and other resource management problems), industrial problems (such as product distribution flow problems) and medical problems (such as patient physiological monitoring). Queen (1994) and Queen et al. (2007b) use an MDM to model monthly brand sales in a competitive market. Here, the competition in the market is the causal drive within the system and is used to define a conditional independence structure across the time series. Queen (1997) and Queen et al. (1994) focus on how the DBN at time t may be elicited for MDMs for modelling markets. Following Whitlock and Queen and , this paper considers (in Section 4) the specific application of forecasting traffic flows at various points in a road network. In this application, the direction of traffic flow produces the causal drive through the system and the possible routes through the system are used to define a conditional independence structure across the time series.

Journal of Multivariate Analysis, 2011

a b s t r a c t showed that the problem min

Computers & Education, 2015

Summary In canonical analysis with more variables than samples, it is shown that, as well as the ... more Summary In canonical analysis with more variables than samples, it is shown that, as well as the usual canonical means in the range-space of the within-groups dispersion matrix, canonical means may be dened in its null space. In the range space we have the usual

Journal of Multivariate Analysis, 2014

ABSTRACT Visualisations of two-way arrays are well-understood. Here, a procedure, with geometric ... more ABSTRACT Visualisations of two-way arrays are well-understood. Here, a procedure, with geometric underpinning, is given for visualising rank-two three-way arrays in two-dimensions.

Studies in Classification, Data Analysis, and Knowledge Organization, 2007

The set of Euclidean distance matrices has a well-known representation as a convex cone. The prob... more The set of Euclidean distance matrices has a well-known representation as a convex cone. The problems of representing the group averages of K distance matrices are discussed, but not fully resolved, in the context of SMACOF, Generalized Orthogonal Procrustes Analysis and Individual Differences Scaling. The polar (or dual) cone representation, corresponding to inner-products around a centroid, is also discussed. Some new characterisations of distance cones in terms of circumhyperspheres are presented.

ABSTRACT . In the article `How To Assign Probabilities if you must'[1] several methods to... more ABSTRACT . In the article `How To Assign Probabilities if you must'[1] several methods to assign probabilities applied to a two-played die rolling game were discussed. The main focus was on methods using the logarithmic loss function for the choice of proper loss function. In this additional part, we will discuss the Brier and Epstein loss functions as an alternative. Futhermore, an extensive graphical display of the situation will be made. For notation and terminology, the reader is referred to Albers et al.[1] 1. Extension to other proper loss functions We consider three proper loss functions: logarithmic (L log ), Brier (LB ) and Epstein (LE ) loss L log (y; Q(x)) = Gamma log q x (y) (1) LB (y; Q(x)) = (1 Gamma q x (y)) 2 + X j 6=y (q x (j)) 2 (2) LE (y; Q(x)) = 6 X j=1 / 1 fy;::: ;6g (j) Gamma j X =1 q x () ! 2 (3) For the seven possible combinations of x and y, the losses incorporated are: x y logarithmic Brier Epstein 1 1 log(2) 0:5 1 1 5 log(2) 0:5 1 2 2 Gamma lo...

Statistical inference is about using statistical data (x) to formulate an opinion about something... more Statistical inference is about using statistical data (x) to formulate an opinion about something that is dened well, but unknown ( y). Testing a hypothesis H about y is one of the possibilities, the estimation or prediction ofy is another one. We concentrate the attention on estimation or prediction in the sense that an opinion is required in the form

Open University Statistics Group …, 2008

The use of intervention for time series modelling is a well established technique for on-line for... more The use of intervention for time series modelling is a well established technique for on-line forecasting and decision-making in the context of Bayesian dynamic linear models. Intervention has also been recently used in (non-dynamic) Bayesian networks to investigate causal relationships between variables, and in dynamic Bayesian networks to investigate lagged causal relationships between time series. The Multiregression Dynamic Model (MDM) is a Bayesian dynamic model and an example of a dynamic Bayesian network. The focus of this paper is the use of intervention in the MDM. It will be demonstrated that not only is intervention in the MDM a powerful tool for forecasting, but intervention can also aid in the identification of contemporaneous causal relationships between time series, thus going beyond the identification of lagged causal relationships previously addressed in dynamic Bayesian networks. 1 The multiregression dynamic model (MDM) (Queen and Smith, 1993) is an example of a DBN. The MDM is a Bayesian dynamic model and is defined to preserve any conditional independences, related to causality, across a multivariate time series over time. At each time t, the observable component series Y t (1), . . . , Y t (n) of the n-dimensional time series, and their associated state vectors θ t (1), . . . , θ t (n), are represented by a BN. These individual BNs are linked together over time to form a DBN. The MDM then uses the conditional independences and causal driving mechanism through the system, as represented by the DBN, to break down the multivariate model into simpler univariate components. There are many potential application areas for the MDM, including problems in economics (modelling various economic indicators such as energy consumption and GDP), environmental problems (such as water and other resource management problems), industrial problems (such as product distribution flow problems) and medical problems (such as patient physiological monitoring). Queen (1994) and Queen et al. (2007b) use an MDM to model monthly brand sales in a competitive market. Here, the competition in the market is the causal drive within the system and is used to define a conditional independence structure across the time series. Queen (1997) and Queen et al. (1994) focus on how the DBN at time t may be elicited for MDMs for modelling markets. Following Whitlock and Queen and , this paper considers (in Section 4) the specific application of forecasting traffic flows at various points in a road network. In this application, the direction of traffic flow produces the causal drive through the system and the possible routes through the system are used to define a conditional independence structure across the time series.

Synthese, 2005

After explaining the well-known two-envelope 'paradox' by indicating the fallacy involved, we con... more After explaining the well-known two-envelope 'paradox' by indicating the fallacy involved, we consider the two-envelope 'problem' of evaluating the 'factual' information provided to us in the form of the value contained by the envelope chosen first. We try to provide a synthesis of contributions from economy, psychology, logic, probability theory (in the form of Bayesian statistics), mathematical statistics (in the form of a decision-theoretic approach) and game theory. We conclude that the two-envelope problem does not allow a satisfactory solution. An interpretation is made for statistical science at large. Synthese (2005) 145: 89-109

Statistics & Decisions, 2000

To test the hypothesis H0 : f = ψ that an unknown density f is equal to a specified one, ψ, an es... more To test the hypothesis H0 : f = ψ that an unknown density f is equal to a specified one, ψ, an estimatef of f is compared with ψ. The total variation distance ||f − ψ|| 1 is used as test statistic.

Statistica Neerlandica, 2001

Journal of the Royal Statistical Society: Series C (Applied Statistics), 2013

Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a mult... more Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting of traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending on the time of day and accommodating measurement errors occurring due to data collection errors. This paper extends LMDMs to address these issues. Additionally, the paper investigates how close the approximate forecast limits usually used with the LMDM are to the true, but not so readily available, forecast limits.