Sequential Generalized Likelihood Ratio Tests for Online Item Monitoring

The study presents statistical procedures that monitor functioning of items over time. We propose generalized likelihood ratio tests that surveil multiple item parameters and implement with various sampling techniques to perform continuous or intermittent monitoring. The procedures examine stability of item parameters across time and inform compromise as soon as they identify significant parameter shift. The performance of the monitoring procedures was validated using simulated and real assessment data. The empirical evaluation suggests that the proposed procedures perform adequately well in identifying the parameter drift. They showed satisfactory detection power and gave timely signals while regulating the error rates reasonably low. The procedures also showed superior performance when compared with the existent methods. The empirical findings suggest that multivariate parametric monitoring can provide an efficient and powerful control tool for maintaining the quality of items. The procedures allow joint monitoring of multiple item parameters and achieve sufficient power by dint of likelihood-ratio tests. Based on the findings from the empirical experimentation, we suggest some practical strategies for performing online item monitoring.

randregret: A command for fitting random regret minimization models using Stata

In this article, we describe the randregret command, which implements a variety of random regret minimization (RRM) models. The command allows the user to apply the classic RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181–196), the generalized RRM model introduced in Chorus (2014, Transportation Research, Part B 68: 224–238), and also the µRRM and pure RRM models, both introduced in van Cranenburgh, Guevara, and Chorus (2015, Transportation Research, Part A 74: 91–109). We illustrate the use of the randregret command by using stated choice data on route preferences. The command offers robust and cluster standarderror correction using analytical expressions of the score functions. It also offers likelihood-ratio tests that can be used to assess the relevance of a given model specification. Finally, users can obtain the predicted probabilities from each model by using the randregretpred command.

Prediction of DNA from context using neural networks

One way to better understand the structure in DNA is by learning to predict the sequence. Here, we train a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model is a neural network that obtains an accuracy close to 54% on the human genome, which is 2% points better than modelling the data using a Markov model. In likelihood-ratio tests, we show that the neural network is significantly better than any of the alternative models by a large margin. We report on where the accuracy is obtained, observing first that the performance appears to be uniform over the chromosomes. The models perform best in repetitive sequences, as expected, although they are far from random performance in the more difficult coding sections, the proportions being ~70:40%. Exploring further the sources of the accuracy, Fourier transforming the predictions reveals weak but clear periodic signals. In the human genome the characteristic periods hint at connections to nucleosome positioning. To understand this we find similar periodic signals in GC/AT content in the human genome, which to the best of our knowledge have not been reported before. On other large genomes similarly high accuracy is found, while lower predictive accuracy is observed on smaller genomes. Only in mouse did we see periodic signals in the same range as in human, though weaker and of different type. Interestingly, applying a model trained on the mouse genome to the human genome results in a performance far below that of the human model, except in the difficult coding regions. Despite the clear outcomes of the likelihood ratio tests, there is currently a limited superiority of the neural network methods over the Markov model. We expect, however, that there is great potential for better modelling DNA using different neural network architectures.

Mixed Effects Models

Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset which has been used throughout the book, although now it is viewed through a new lens. Readers are taught how to code likelihood ratio tests to calculate statistical significance and how to use multiple packages, such as lme4 and glmmTMB.

Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration

Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration

Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration

Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration