Modeling and Comparing Vertical Density Profiles

The vertical density profile of pressed wood panels is influenced by the manufacturing process and is important to panel end-users. Modeling the vertical density profile and making statistical comparisons among profiles resulting from different manufacturing treatments are critical to understanding and improving panel properties. Nonparametric regression analysis was used to model the vertical density profile of aspen (Populus tremuloides) oriented strandboard panels. A methodology is presented to compare vertical density profile curves. Twenty-seven laboratory panels were manufactured at 608 kg/m3 incorporating three levels of furnish moisture content (4%, 8%, 12%) and three levels of press closure rate (20 s, 60 s, 100 s) in a replicated, experimental design.The nonparametric regression technique called cubic splines was used to fit the data, R2 ranged from 0.985 to 0.998. Detailed discussion is presented that describes the method and interpretation of the nonparametric regression analysis. Statistical comparison of vertical density profile curves among treatment levels revealed that the 4% furnish moisture content level was significantly different (P = 0.015) from the 8% and 12% levels; the 8% level was not significantly different (P > 0.99) from the 12% level. Vertical density profile curves for all press closure rate treatments were significantly different (P < 0.003)

Alternative smoothing strategies in smooth partial least squares path modelling

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMThe assessment of nonlinear relationships in the context of Partial Least Squares Path Modelling (PLS-PM) has received a growing interest in recent years. One important contribution to this subject has been the work of Henseler, Fassot, Dijkstra and Wilson (2012) on the analysis of four different approaches to quadratic effects. The Smooth Partial Least Squares (PLSs) estimation technique studied in this work removes any assumptions on the structure of the nonlinear relationships between latent variables, by applying smoothing spline techniques to the structural model. Performance results of the PLSs show that it is a powerful tool in the context of predictive research, for instance to support the definition of targeted policies. Building from the hybrid approach to the PLS algorithm introduced by Wold (1982), we compare the performance of alternative spline designs, including natural cubic splines, P-Splines and Thin Plate Regression Splines (TPRS). For this purpose, Monte-Carlo simulations are carried with a conceptual model drawn from a comprehensive set of nonlinear relationships, in different sample sizes. All model configurations are compared using Root Mean Squared Error (RMSE) and absolute bias results. The benchmarking exercise shows that, in most contexts, P-Splines perform slightly better than TPRS and natural cubic splines

Discussion of 'Beyond mean regression'

Methodology for regression beyond the mean has been a goal of researchers for many years. This discussion provides some additional context for the important ideas in the present paper, by recounting some of the historical background to the GAMLSS approach and pointing to the power and appeal of fully probabilistic regression analysis in the setting of Bayesian nonparametrics. © 2013 SAGE Publications

A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

Most Likely Transformations

We propose and study properties of maximum likelihood estimators in the class of conditional transformation models. Based on a suitable explicit parameterisation of the unconditional or conditional transformation function, we establish a cascade of increasingly complex transformation models that can be estimated, compared and analysed in the maximum likelihood framework. Models for the unconditional or conditional distribution function of any univariate response variable can be set-up and estimated in the same theoretical and computational framework simply by choosing an appropriate transformation function and parameterisation thereof. The ability to evaluate the distribution function directly allows us to estimate models based on the exact likelihood, especially in the presence of random censoring or truncation. For discrete and continuous responses, we establish the asymptotic normality of the proposed estimators. A reference software implementation of maximum likelihood-based estimation for conditional transformation models allowing the same flexibility as the theory developed here was employed to illustrate the wide range of possible applications.Comment: Accepted for publication by the Scandinavian Journal of Statistics 2017-06-1

Forecasting and Risk Management Techniques for Electricity Markets

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects