Overview of methods to analyse dynamic data

This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

A method for Bayesian regression modelling of composition data

Many scientific and industrial processes produce data that is best analysed as vectors of relative values, often called compositions or proportions. The Dirichlet distribution is a natural distribution to use for composition or proportion data. It has the advantage of a low number of parameters, making it the parsimonious choice in many cases. In this paper we consider the case where the outcome of a process is Dirichlet, dependent on one or more explanatory variables in a regression setting. We explore some existing approaches to this problem, and then introduce a new simulation approach to fitting such models, based on the Bayesian framework. We illustrate the advantages of the new approach through simulated examples and an application in sport science. These advantages include: increased accuracy of fit, increased power for inference, and the ability to introduce random effects without additional complexity in the analysis.Comment: 10 pages, 1 figure, 2 table

Evolution of statistical analysis in empirical software engineering research: Current state and steps forward

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001--2015 and 5,196 papers. Results from both review steps was used to: i) identify and analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context.Comment: journal submission, 34 pages, 8 figure

Spatial Smoothing Techniques for the Assessment of Habitat Suitability

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of the observations is often violated in practice since the points at which the observations are collected are spatially aligned. While several approaches have been developed to analyse and account for spatial correlation in regression models with normally distributed responses, far less work has been done in the context of generalised linear models. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations

A General Approach for the Analysis of Habitat Selection

Investigating habitat selection of animals aims at the detection of preferred and avoided habitat types as well as at the identification of covariates influencing the choice of certain habitat types. The final goal of such analyses is an improvement of the conservation of animals. Usually, habitat selection by larger animals is assessed by radio-tracking or visual observation studies, where the chosen habitat is determined for a number of animals at a set of time points. Hence the resulting data often have the following structure: A categorical variable indicating the habitat type selected by an animal at a specific time point is repeatedly observed and shall be explained by covariates. These may either describe properties of the habitat types currently available and / or properties of the animal. In this paper, we present a general approach for the analysis of such data in a categorical regression setup. The proposed model generalises and improves upon several of the approaches previously discussed in the literature and in particular allows to account for changing habitat availability due to the movement of animals within the observation area. It incorporates both habitat- and animal-specific covariates, and includes individual-specific random effects in order to account for correlations introduced by the repeated measurements on single animals. The methodology is implemented in a freely available software package. We demonstrate the general applicability and the capabilities of the proposed approach in two case studies: The analysis of a songbird in South-America and a study on brown bears in Central Europe

Assessment of leaf cover and crop soil cover in weed harrowing research using digital images

Objective assessment of crop soil cover, defined as the percentage of leaf cover that has been buried in soil due to weed harrowing, is crucial to further progress in post-emergence weed harrowing research. Up to now, crop soil cover has been assessed by visual scores, which are biased and context dependent. The aim of this study was to investigate whether digital image analysis is a feasible method to estimate crop soil cover in the early growth stages of cereals. Two main questions were examined: (1) how to capture suitable digital images under field conditions with a standard high-resolution digital camera and (2) how to analyse the images with an automated digital image analysis procedure. The importance of light conditions, camera angle, size of recorded area, growth stage and direction of harrowing were investigated in order to establish a standard for image capture and an automated image analysis procedure based on the excess green colour index was developed. The study shows that the automated digital image analysis procedure provided reliable estimations of leaf cover, defined as the as the proportion of pixels in digital images determined to be green, which were used to estimate crop soil cover. A standard for image capture is suggested and it is recommended to use digital image analysis to estimated crop soil cover in future research. The prospects of using digital image analysis in future weed harrowing research are discussed

A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment

Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis

Effective forecasting for supply-chain planning: an empirical evaluation and strategies for improvement

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a simple univariate statistical method to produce a forecast and the subsequent judgmental adjustment of this by the company's demand planners to take into account market intelligence relating to any exceptional circumstances expected over the planning horizon. Based on four company case studies, which included collecting more than 12,000 forecasts and outcomes, this paper examines: i) the extent to which the judgmental adjustments led to improvements in accuracy, ii) the extent to which the adjustments were biased and inefficient, iii) the circumstances where adjustments were detrimental or beneficial, and iv) methods that could lead to greater levels of accuracy. It was found that the judgmentally adjusted forecasts were both biased and inefficient. In particular, market intelligence that was expected to have a positive impact on demand was used far less effectively than intelligence suggesting a negative impact. The paper goes on to propose a set of improvements that could be applied to the forecasting processes in the companies and to the forecasting software that is used in these processes

Neural nets - their use and abuse for small data sets

Neural nets can be used for non-linear classification and regression models. They have a big advantage over conventional statistical tools in that it is not necessary to assume any mathematical form for the functional relationship between the variables. However, they also have a few associated problems chief of which are probably the risk of over-parametrization in the absence of P-values, the lack of appropriate diagnostic tools and the difficulties associated with model interpretation. The first of these problems is particularly important in the case of small data sets. These problems are investigated in the context of real market research data involving non-linear regression and discriminant analysis. In all cases we compare the results of the non-linear neural net models with those of conventional linear statistical methods. Our conclusion is that the theory and software for neural networks has some way to go before the above problems will be solved