Multilevel data and Bayesian analysis in traffic safety

Multilevel data structures are those that have a hierarchical structure, in which response variables are measured at the lowest level of the hierarchy and modeled as a function of predictor variables measured at that level and higher levels of the hierarchy. For example, a multilevel data structure may consist of measurements taken on individual fish (lower level) that are nested within lakes or streams (higher level). Multilevel data structures are a common feature in fisheries research. We provide simulated fisheries data examples, similar in structure to other published studies, to illustrate the application of multilevel models and discuss how hypothesis testing and inferences can be incorrect if multilevel data structures are ignored. Ignoring multilevel data structures has implications for the use of commonly-used ordinary least squares (OLS) approaches to test hypotheses and to make inferences. Multilevel models are an alternate approach that circumvents problems associated with traditional OLS methods and allows valid inferences to be made.

Multilevel data structures are known as consisting of multiple units of analysis, one clustered within the other. The concept of multilevel data modelling has been developed for several years mainly because the researchers have realized the disadvantages of ignoring such multilevel data structures. This study aims to find out factors affecting the General Certificate of Education Ordinary Level (G.C.E. O/L) pass rate at schools located in civil war affected provinces in Sri Lanka. The study also extends to observe the multilevel data structure by schools, districts and provinces, and determine how these levels have an impact on the G.C.E. O/L pass rate. The above has been undertaken by the application of advanced analysis focused on developing Generalized Linear Multilevel Model for ordered categorical response using the Bayesian Markov Chain Monte Carlo estimation method employing MLwiN 2.10 software. Finally, the partial non-proportional odds model was selected as the most appropriate model for the Educational data used in this study based on account of its simplicity and accuracy.

A stepwise method of deriving the high-performance implementation of a set of operations is proposed. This method is based on the ability to organize the data into a multilevel data structure to provide an efficient implementation of all the operations. Typically, for such data organization the performance may deteriorate over a period of time and that can be corrected by reorganizing the data. This data reorganization is done by the introduction of maintenance processes. For a particular example, the multilevel data organization and the different models of maintenance processes possible are considered. The various models of maintenance process provide varying amounts of concurrency by varying the degree of atomicity in different operations. Performance behavior for the different models is derived and a correctness proof for the developed implementation is outlined.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

BackgroundAutomated feature selection methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) have recently gained importance in the prediction of quality-related outcomes as well as the risk-adjustment of quality indicators in healthcare. The methods that have been used so far, however, do not account for the fact that patient data are typically nested within hospitals.MethodsTherefore, we aimed to demonstrate how to account for the multilevel structure of hospital data with LASSO and compare the results of this procedure with a LASSO variant that ignores the multilevel structure of the data. We used three different data sets (from acute myocardial infarcation, COPD, and stroke patients) with two dependent variables (one numeric and one binary), on which different LASSO variants with and without consideration of the nested data structure were applied. Using a 20-fold sub-sampling procedure, we tested the predictive performance of the different LASSO variants and examined differences in variable importance.ResultsFor the metric dependent variable Duration Stay, we found that inserting hospitals led to better predictions, whereas for the binary variable Mortality, all methods performed equally well. However, in some instances, the variable importances differed greatly between the methods.ConclusionWe showed that it is possible to take the multilevel structure of data into account in automated predictor selection and that this leads, at least partly, to better predictive performance. From the perspective of variable importance, including the multilevel structure is crucial to select predictors in an unbiased way under consideration of the structural differences between hospitals.

Group similarities and differences may manifest themselves in a variety of ways in multiple-group latent class analysis (LCA). Sometimes, measurement models are identical across groups in LCA. In other situations, the measurement models may differ, suggesting that the latent structure itself is different between groups. Tests of measurement invariance shed light on this distinction. We created an R package glca that implements procedures for exploring differences in latent class structure between populations, taking multilevel data structure into account. The glca package deals with the fixed-effect LCA and the nonparametric random-effect LCA; the former can be applied in the situation where populations are segmented by the observed group variable itself, whereas the latter can be used when there are too many levels in the group variable to make a meaningful group comparisons by identifying a group-level latent variable. The glca package consists of functions for statistical test procedures for exploring group differences in various LCA models considering multilevel data structure.

To construct CFA, MCFA, and maximum MCFA with LISREL v.8 and below, we provide iMCFA (integrated Multilevel Confirmatory Analysis) to examine the potential multilevel factorial structure in the complex survey data. Modeling multilevel structure for complex survey data is complicated because building a multilevel model is not an infallible statistical strategy unless the hypothesized model is close to the real data structure. Methodologists have suggested using different modeling techniques to investigate potential multilevel structure of survey data. Using iMCFA, researchers can visually set the between- and within-level factorial structure to fit MCFA, CFA and/or MAX MCFA models for complex survey data. iMCFA can then yield between- and within-level variance-covariance matrices, calculate intraclass correlations, perform the analyses and generate the outputs for respective models. The summary of the analytical outputs from LISREL is gathered and tabulated for further model comparison and interpretation. iMCFA also provides LISREL syntax of different models for researchers' future use. An empirical and a simulated multilevel dataset with complex and simple structures in the within or between level was used to illustrate the usability and the effectiveness of the iMCFA procedure on analyzing complex survey data. The analytic results of iMCFA using Muthen's limited information estimator were compared with those of Mplus using Full Information Maximum Likelihood regarding the effectiveness of different estimation methods.

Multilevel data structures are ubiquitous in the assessment of differential item functioning (DIF), particularly in large-scale testing programs. There are a handful of DIF procures for researchers to select from that appropriately account for multilevel data structures. However, little, if any, work has been completed to extend a popular DIF method to this case. Thus, the primary goal of this study was to introduce and investigate the effectiveness of several new options for DIF assessment in the presence of multilevel data with the Mantel–Haenszel (MH) procedure, a popular, flexible, and effective tool for DIF detection. The performance of these new methods was compared with the standard MH technique through a simulation study, where data were simulated in a multilevel framework, corresponding to examinees nested in schools, for example. The standard MH test for DIF detection was employed, along with several multilevel extensions of MH. Results demonstrated that these multilevel tests proved to be preferable to standard MH in a wide variety of cases where multilevel data were present, particularly when the intraclass correlation was relatively large. Implications of this study for practice and future research are discussed.

Development of mass customised products demands various activities in the product development process, such as design, manufacturing process planning, manufacturing resource planning and maintenance process planning, to be considered and coordinated. In this research, a multi-population co-evolutionary genetic programming (MCGP) approach is introduced to identify the optimal design and its downstream product life cycle activities for developing mass customised product considering these different product life cycle activities and their relationships. In this research, two types of relationships between downstream product life cycle activities are considered: sequential relationships and concurrent relationships. The product design and its downstream life cycle descriptions are modelled by a multi-level graph data structure. These product life cycle descriptions are defined at two different levels: generic level for modelling the descriptions in a product family and specific level for modelling the descriptions of a customised product. The optimal design and its downstream life cycle activities are identified through the MCGP approach based on evaluations in different product life cycle aspects. Various methods have been developed to improve computation efficiency for the MCGP. Industrial case studies and comparative case studies have been implemented to demonstrate the effectiveness of the developed approach.

Many multilevel linear and item response theory models have been developed to account for multilevel data structures. However, most existing cognitive diagnostic models (CDMs) are unilevel in nature and become inapplicable when data have a multilevel structure. In this study, using the log-linear CDM as the item-level model, multilevel CDMs were developed based on the latent continuous variable approach and the multivariate Bernoulli distribution approach. In a series of simulations, the newly developed multilevel deterministic input, noisy, and gate (DINA) model was used as an example to evaluate the parameter recovery and consequences of ignoring the multilevel structures. The results indicated that all parameters in the new multilevel DINA were recovered fairly well by using the freeware Just Another Gibbs Sampler (JAGS) and that ignoring multilevel structures by fitting the standard unilevel DINA model resulted in poor estimates for the student-level covariates and underestimated standard errors, as well as led to poor recovery for the latent attribute profiles for individuals. An empirical example using the 2003 Trends in International Mathematics and Science Study eighth-grade mathematical test was provided.

In this paper, we consider several efficient data structures for the problem of sampling from a dynamically changing discrete probability distribution, where some prior information is known on the distribution of the rates, in particular the maximum and minimum rate, and where the number of possible outcomes N is large. We consider three basic data structures, the Acceptance–Rejection method, the Complete Binary Tree and the Alias method. These can be used as building blocks in a multi-level data structure, where at each of the levels, one of the basic data structures can be used, with the top level selecting a group of events, and the bottom level selecting an element from a group. Depending on assumptions on the distribution of the rates of outcomes, different combinations of the basic structures can be used. We prove that for particular data structures the expected time of sampling and update is constant when the rate distribution follows certain conditions. We show that for any distribution, combining a tree structure with the Acceptance–Rejection method, we have an expected time of sampling and update of Oleft( log log {r_{max}}/{r_{min}}right) is possible, where r_{max} is the maximum rate and r_{min} the minimum rate. We also discuss an implementation of a Two Levels Acceptance–Rejection data structure, that allows expected constant time for sampling, and amortized constant time for updates, assuming that r_{max} and r_{min} are known and the number of events is sufficiently large. We also present an experimental verification, highlighting the limits given by the constraints of a real-life setting.

When discrete response models arc estimated with multilevel, or clustered, data, there is much interest in the distribution of the residuals. With logistic regression it is usually assumed that these residuals are binomially distributed. A common real world situation is where the multilevel data structure is sparse, with relatively few individual cases per cluster. The impact of sparsity on estimates of binomial variation is explored using simulation techniques. This exploration demonstrates that sparsity leads to extra‐binomial variation. The results are discussed in relation to recent data on eyewitness line‐ups, an example with a sparse data structure.

Coalition research increasingly emphasizes party-level explanations of coalition outcomes. However, this work does not account for the complex multilevel structure between parties and governments: many parties participate in multiple governments and governments often comprise multiple parties. In this paper, I show that this crisscrossing structure creates dependencies among observations both across and within governments. If ignored, these dependencies produce downward-biased uncertainty estimates that cluster-robust standard errors fail to fully correct. To address this issue, I then introduce a model that extends the Multiple Membership Multilevel Model to represent the multilevel structure of coalition government data. The model accounts for party-level dependencies across governments through party-specific effects in each coalition they join, and for dependencies within governments by representing the total party effect on a government as a weighted sum of its members’ contributions. By allowing party weights to vary with covariates describing their interrelationships, the model enables researchers to examine the interdependent nature of coalition outcomes. I validate the model through simulation and an empirical application to coalition government survival, showing that ignoring party-level dependencies can produce misleading conclusions at all levels of analysis. The model is estimated via Bayesian MCMC and implemented in the accompanying R package ‘bml’.

We investigate an implementation of the multi-level or ℓ-level k-range data structure. The ℓ-level k-range is compared to naive and R*tree search over N randomly generated k-dimensional points. Results indicate that multi-level k-ranges are not competitive due to their (previously unreported) complexity. We show that storage is S(N,k,ℓ) = O(N1+2(k-1)/ℓ) and S(N,k) = Θ(N1+2(k-1)/ log 2N). Our results also indicate that the ℓ-level k-range requires Q(N,k,ℓ) = O((2ℓ)k( log N + A)) time for range search, for A = number of points reported in range.

Air pollution has become an important factor constraining city development and threatening public health in recent years. Air pollution prediction has been considered as the key part for the early warning of pollution event. Considering the multi-scale nature of geo-sensory data such as air pollution signal, in this paper we adopt a multi-level graph data structure for better utilization of multi-scale spatio-temporal information. We further present a novel deep convolutional neural network model, named Multi-Scale Spatial Temporal Network (MSSTN), for the learning task on this data structure. The MSSTN is specially designed to better discover multi-scale spatial temporal patterns and their high-level interactions, by explicitly using multi-scale neural network structure in both spatial and temporal component. We conduct extensive experiments and ablation studies on Urban Air Pollution Datasets in North China, where the MSSTN can make hourly PM2.5 concentration predictions jointly for a number of cities. And our results shows an outstanding prediction accuracy as well as high computational efficiency compared to existing works.

We review shortest path algorithms based on the multi-level bucket data structure [6] and discuss the interplayb etween theorya nd engineering choices that leads to efficient implementations. Our experimental results suggest that the caliber heuristic [17] and adaptive parameter selection give an efficient algorithm, both on typical and on hard inputs, for a wide range of arc lengths.