We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models (GLMMs). Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a well researched statistical framew...

The above concept brings out the method for the multilevel inverters for generation of space vector pulse width modulation (SVPWM) signals. The small triangles formed by the adjacent voltage space vectors are called sectors. Such six sectors around a voltage space vector forms a hexagon called sub hexagon. The space vector diagram of a multilevel inverter can be viewed as composed of a number o...

Predicting changes in community composition and ecosystem function in a rapidly changing world is a major research challenge in ecology. Traits-based approaches have elicited much recent interest, yet individual studies are not advancing a more general, predictive ecology. Significant progress will be facilitated by adopting a coherent theoretical framework comprised of three elements: an under...

Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in th...

In this paper a two-level regression model is imposed on the ability parameters in an IRT model. The advantage of using latent rather than observed scores as dependent variables of a multi-level model is that this offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to o...

Single-level multiple imputation procedures (e.g., PROC MI) are not appropriate for multilevel data sets where observations are nested within clusters. Analyzing multilevel data imputed with a single-level procedure yields variance estimates that are biased toward zero and may yield other biased parameters. Given the prevalence of clustered data (e.g., children within schools; employees within ...

In the paper we discuss applications of the Bayesian approach to new challenges in relevance analysis. Earlier, we formulated a Bayesian approach to Feature Subset Selection using Bayesian networks to jointly estimate the posteriors of Markov Blanket Memberships (MBMs), Markov Blanket Sets (MBSs), and Markov Blanket Graphs (MBGs) for a given target variable. These results of the Bayesian Multil...

Earlier, we formulated a Bayesian approach to Feature Subset Selection using Bayesian networks, which jointly estimate the posteriors of Markov Blanket Memberships (MBMs), Markov Blanket Sets (MBSs), and Markov Blanket Subgraphs (MBGs) for a given target variable. These results of the Bayesian Multilevel Analysis of relevance (BMLA) correspond respectively to a model-based pairwise relevance, r...

In power-limited Wireless Sensor Network (WSN), it is important to reduce the communication load in order to achieve energy savings. This paper applies a novel statistic method to estimate the parameters based on the realtime data measured by local sensors. Instead of transmitting large real-time data, we proposed to transmit the small amount of dynamic parameters by exploiting both temporal an...