346 research outputs found
Nonparametric inference in hidden Markov models using P-splines
Hidden Markov models (HMMs) are flexible time series models in which the
distributions of the observations depend on unobserved serially correlated
states. The state-dependent distributions in HMMs are usually taken from some
class of parametrically specified distributions. The choice of this class can
be difficult, and an unfortunate choice can have serious consequences for
example on state estimates, on forecasts and generally on the resulting model
complexity and interpretation, in particular with respect to the number of
states. We develop a novel approach for estimating the state-dependent
distributions of an HMM in a nonparametric way, which is based on the idea of
representing the corresponding densities as linear combinations of a large
number of standardized B-spline basis functions, imposing a penalty term on
non-smoothness in order to maintain a good balance between goodness-of-fit and
smoothness. We illustrate the nonparametric modeling approach in a real data
application concerned with vertical speeds of a diving beaked whale,
demonstrating that compared to parametric counterparts it can lead to models
that are more parsimonious in terms of the number of states yet fit the data
equally well
Reliability Analysis of Complex Systems with Failure Propagation
Failure propagation is a critical factor for the reliability and safety of complex systems. To recognise and identify failure propagation of systems, a deep fusion model based on deep belief network (DBN) and Bayesian structural equation model (BSEM) is proposed. The deep belief network is applied to extract features between status monitoring data and the performance degradation in different failure components. To calculate the path weight of failure propagation, the Bayesian structural equation model is proposed to study the relationship among different fault modes. After getting the performance degradation of each fault through DBN and calculating the path weight of fault propagation by BSEM, it is available to get the overall reliability of the system. The aircraft landing gear system with 19 fault patterns is selected to evaluate the feasibility of the proposed deep fusion model. The results demonstrate that the overall reliability of the system can be obtained by analysing the fault propagation of multiple fault patterns, and the proposed model has a lower deviation than traditional back propagation neural network
ISBIS 2016: Meeting on Statistics in Business and Industry
This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647.
The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by:
David Banks, Duke University
AmÃlcar Oliveira, DCeT - Universidade Aberta and CEAUL
Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL
Nalini Ravishankar, University of Connecticut
Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH
Martina Vandebroek, KU Leuven
Vincenzo Esposito Vinzi, ESSEC Business Schoo
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
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Bayesian partition models for local inference in longitudinal and survival data
This dissertation proposes novel Bayesian semiparametric and nonparametric methods for complex, large and potentially high-dimensional longitudinal and survival data. The first part, comprising the bulk of this thesis, develops sophisticated dynamic partition models for longitudinal data that allow common features to be shared across some time segments while differing across others. These ideas are then specifically adapted to develop novel drift-diffusion models for the analysis of behavioral data on category learning in auditory neuroscience. The second part of this work proposes a bivariate survival regression method, borrowing information across two outcomes via common features in parts of the induced marginal partitions. In terms of flexibility and interpretability, the methods presented here provide significant improvements over many previously available tools and techniques, leading to interesting, novel and meaningful inference in many diverse application areas.Statistic
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