In previous chapters, we have studied the model 

$$y = A\beta + \epsilon, $$

where the mean Ey = Aβ depends linearly on the parameters β, the errors are normal (Gaussian), and the errors are additive. We have also seen (Chapter 7) that in some situations, a transformation of the problem may help to correct some departure from our standard model assumptions. For example, in §7.3 on variance-stabilising transformations, we transformed our data from y to some function g(y), to make the variance constant (at least approximately). We did not there address the effect on the error structure of so doing. Of course, $g(y) = g(A\beta + \epsilon )$ as above will not have an additive Gaussian error structure any more, even approximately, in general.

Generalised Linear Models

This paper applies the relatively new method of latent transition analysis within the mobility biographies framework to assess how life events influence changes in travel behaviour Using transition analysis, it is assessed how people switch between different travel patterns over time Data from the first three waves of the Netherlands Mobility Panel (MPN) are used to reveal different travel patterns and analyse transitions between these patterns over time Six different meaningful travel patterns are revealed Four exogenous variables and six life events within the household, employment and residential biography are included to assess their effects on people’s transitions between the travel patterns over time For all life events significant effects are found, indicating that there might indeed be ‘windows of opportunity’ to change travel behaviour when a life event occurs The results show that, on average, people who only use a single mode are less likely to change their travel pattern compared to multimodal travellers In addition, the effects of life events and exogenous variables depend on the initial travel pattern In general, single-mode travellers are less affected by life events than multimodal travellers This indicates that it is important to include past travel behaviour within mobility biographies studies

Travel Pattern Transitions: Applying Latent Transition Analysis Within the Mobility Biographies Framework

Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE) The results showed that the incorporation of periodicity (ie, MN) as an additional input variable considerably improved both the training performance and predictive performance of the models A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world

Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm

Higgs Boson Discovery using Machine Learning Methods with Pyspark

Ensemble methods are a machine learning technique that combines several base models in order to produce one optimal predictive model In this paper, we compare accuracy metric of three ensemble methods: Bagging, Random Forest, and Boosting Then, We use the “CARET package”, implemented in R language, to experiment the Time Resolution Universe (HTRU2) dataset, obtained from UCI Machine Learning Repository

Using Ensemble Methods to Solve the Problem of Pulsar Search

Detection of Pulsar Candidates using Bagging Method

Latent Transition Analysis (LTA) : A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups

The correct selection of the number of classes represents a difficult step in classification methods because it can significantly affect interpretations and may lead to an incorrect analysis of the studied phenomenon. This problem arises in latent class analysis, which is a method of classifying individuals based on categorical data. Determining the number of classes constituting the profiles of a population is generally done by using a likelihood ratio test, but the use of this test is not correct theoretically. To solve this problem, we use the information criteria to select the number of classes. Several information criteria have been proposed for this purpose previously, but the difficulty of selection remains because these criteria are influenced by other parameters such as the number of classes and the sample size. In this paper, we conduct a comparative study of these information criteria and use a panorama of the best-adapted criteria according to the data for an exact selection of models.

Mourad Azhari

Papers

Higgs Boson Discovery using Machine Learning Methods with Pyspark

Using Ensemble Methods to Solve the Problem of Pulsar Search

Detection of Pulsar Candidates using Bagging Method

Latent Transition Analysis (LTA) : A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups

Solving the problem of latent class selection