The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Robert J Palisano - One of the best experts on this subject based on the ideXlab platform.
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a Multivariate Model of determinants of change in gross motor abilities and engagement in self care and play of young children with cerebral palsy
Physical & Occupational Therapy in Pediatrics, 2011Co-Authors: Lisa A Chiarello, Robert J Palisano, Doreen J Bartlett, Sarah Westcott MccoyAbstract:ABSTRACTA Multivariate Model of determinants of change in gross-motor ability and engagement in self-care and play provides physical and occupational therapists a framework for decisions on interventions and supports for young children with cerebral palsy and their families. Aspects of the child, family ecology, and rehabilitation and community services may influence children's activity and participation. Aspects of the child include primary and secondary impairments, associated and comorbid health conditions, and adaptive behaviors. Literature support for the Model is reviewed. A clinical scenario illustrates the use of the Model as a framework for practice. The Model encourages therapists to broaden the focus of rehabilitation services for young children with CP to include not only development of motor abilities but also comprehensive interventions and supports to enhance participation in daily activities and routines. Therapists are encouraged to consider how child, family, and service factors interact...
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a Multivariate Model of determinants of motor change for children with cerebral palsy
Physical Therapy, 2000Co-Authors: Doreen J Bartlett, Robert J PalisanoAbstract:The purpose of this article is to describe the development of a theory-and data-based Model of determinants of motor change for children with cerebral palsy. The dimensions of human functioning proposed by the World Health Organization, general systems theory, theories of human ecology, and a philosophical approach incorporating family-centered care provide the conceptual framework for the Model. The Model focuses on relationships among child characteristics (eg, primary and secondary impairments, personality), family ecology (eg, dynamics of family function), and health care services (eg, availability, access, intervention options). Clarification of the complex Multivariate and interactive relationships among the multiple child and family determinants, using statistical methods such as structural equation Modeling, is necessary before determining how physical therapy intervention can optimize motor outcomes of children with cerebral palsy. We propose that the development and testing of Multivariate Models is also useful in physical therapy research and in the management of complex chronic conditions other than cerebral palsy. Testing of similar Models could provide physical therapists with support for: (1) prognostic discussions with clients and their families, (2) establishment of realistic and attainable goals, and (3) interventions to enhance outcomes for individual clients with a variety of prognostic attributes.
Zhihua Qu - One of the best experts on this subject based on the ideXlab platform.
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Multivariate predictive analytics of wind power data for robust control of energy storage
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Hamed Valizadeh Haghi, Saeed Lotfifard, Zhihua QuAbstract:Short-term forecasting is frequently identified as an important tool for the effective management of wind generation. However, forecasting errors, inherent to the point forecasts, increase requirements for energy storage and can affect optimal system operation. Probabilistic forecasts can help tackle this issue by providing a proper characterization of forecasting errors in the optimization process. This paper proposes a Multivariate Model of forecasting data for wind generation. Predictive uncertainty intervals of wind power can be obtained by sampling from the proposed Model. The main goal is to use empirical data Models without linear or Gaussian approximations of the distributional or temporal variations. The predictive Modeling is utilized within a case study of an energy storage system. A modified robust convex programming is used to maintain the practical robustness and feasibility of the solution based on the sampled scenarios from the Model.
Doreen J Bartlett - One of the best experts on this subject based on the ideXlab platform.
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a Multivariate Model of determinants of change in gross motor abilities and engagement in self care and play of young children with cerebral palsy
Physical & Occupational Therapy in Pediatrics, 2011Co-Authors: Lisa A Chiarello, Robert J Palisano, Doreen J Bartlett, Sarah Westcott MccoyAbstract:ABSTRACTA Multivariate Model of determinants of change in gross-motor ability and engagement in self-care and play provides physical and occupational therapists a framework for decisions on interventions and supports for young children with cerebral palsy and their families. Aspects of the child, family ecology, and rehabilitation and community services may influence children's activity and participation. Aspects of the child include primary and secondary impairments, associated and comorbid health conditions, and adaptive behaviors. Literature support for the Model is reviewed. A clinical scenario illustrates the use of the Model as a framework for practice. The Model encourages therapists to broaden the focus of rehabilitation services for young children with CP to include not only development of motor abilities but also comprehensive interventions and supports to enhance participation in daily activities and routines. Therapists are encouraged to consider how child, family, and service factors interact...
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a Multivariate Model of determinants of motor change for children with cerebral palsy
Physical Therapy, 2000Co-Authors: Doreen J Bartlett, Robert J PalisanoAbstract:The purpose of this article is to describe the development of a theory-and data-based Model of determinants of motor change for children with cerebral palsy. The dimensions of human functioning proposed by the World Health Organization, general systems theory, theories of human ecology, and a philosophical approach incorporating family-centered care provide the conceptual framework for the Model. The Model focuses on relationships among child characteristics (eg, primary and secondary impairments, personality), family ecology (eg, dynamics of family function), and health care services (eg, availability, access, intervention options). Clarification of the complex Multivariate and interactive relationships among the multiple child and family determinants, using statistical methods such as structural equation Modeling, is necessary before determining how physical therapy intervention can optimize motor outcomes of children with cerebral palsy. We propose that the development and testing of Multivariate Models is also useful in physical therapy research and in the management of complex chronic conditions other than cerebral palsy. Testing of similar Models could provide physical therapists with support for: (1) prognostic discussions with clients and their families, (2) establishment of realistic and attainable goals, and (3) interventions to enhance outcomes for individual clients with a variety of prognostic attributes.
Lawrence Jopseph - One of the best experts on this subject based on the ideXlab platform.
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using a flexible Multivariate latent class approach to Model correlated outcomes a joint analysis of pedestrian and cyclist injuries
Analytic Methods in Accident Research, 2017Co-Authors: Shahram Heydari, Liping Fu, Luis F Mirandamoreno, Lawrence JopsephAbstract:Abstract Several recent transportation safety studies have indicated the importance of accounting for correlated outcomes, for example, among different crash types, including differing injury-severity levels. In this paper, we discuss inference for such data by introducing a flexible Bayesian Multivariate Model. In particular, we use a Dirichlet process mixture to keep the dependence structure unconstrained, relaxing the usual homogeneity assumptions. The resulting Model collapses into a latent class Multivariate Model that is in the form of a flexible mixture of Multivariate normal densities for which the number of mixtures (latent components) not only can be large but also can be inferred from the data as part of the analysis. Therefore, besides accounting for correlation among crash types through a heterogeneous correlation structure, the proposed Model helps address unobserved heterogeneity through its latent class component. To our knowledge, this is the first study to propose and apply such a Model in the transportation literature. We use the Model to investigate the effects of various factors such as built environment characteristics on pedestrian and cyclist injury counts at signalized intersections in Montreal, Modeling both outcomes simultaneously. We demonstrate that the homogeneity assumption of the standard Multivariate Model does not hold for the dataset used in this study. Consequently, we show how such a spurious assumption affects predictive performance of the Model and the interpretation of the variables based on marginal effects. Our flexible Model better captures the underlying complex structure of the correlated data, resulting in a more accurate Model that contributes to a better understanding of safety correlates of non-motorist road users. This in turn helps decision-makers in selecting more appropriate countermeasures targeting vulnerable road users, promoting the mobility and safety of active modes of transportation.
Hamed Valizadeh Haghi - One of the best experts on this subject based on the ideXlab platform.
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Multivariate predictive analytics of wind power data for robust control of energy storage
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Hamed Valizadeh Haghi, Saeed Lotfifard, Zhihua QuAbstract:Short-term forecasting is frequently identified as an important tool for the effective management of wind generation. However, forecasting errors, inherent to the point forecasts, increase requirements for energy storage and can affect optimal system operation. Probabilistic forecasts can help tackle this issue by providing a proper characterization of forecasting errors in the optimization process. This paper proposes a Multivariate Model of forecasting data for wind generation. Predictive uncertainty intervals of wind power can be obtained by sampling from the proposed Model. The main goal is to use empirical data Models without linear or Gaussian approximations of the distributional or temporal variations. The predictive Modeling is utilized within a case study of an energy storage system. A modified robust convex programming is used to maintain the practical robustness and feasibility of the solution based on the sampled scenarios from the Model.
