IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 01/2013, Volume 24, Issue 1, pp. 118 - 132
Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five,...
Neurons | Bifurcation | Stability analysis | Mathematical model | Hopf bifurcation | neural network | Biological neural networks | Delay | Bidirectional associative memory | Neural network | EXISTENCE | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | EXPONENTIAL STABILITY | TIME-VARYING DELAYS | 2-NEURON SYSTEM | GLOBAL ASYMPTOTIC STABILITY | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | delay | DISTRIBUTED DELAYS | DYNAMICS | NEURONS | COMPUTER SCIENCE, THEORY & METHODS | Algorithms | Information Storage and Retrieval - methods | Time Factors | Feedback | Memory | Models, Neurological | Nonlinear Dynamics | Neural Networks (Computer) | Delay lines | Usage | Numerical analysis | Neural networks | Simulation methods | Innovations | Studies | Economic models | Nonlinear dynamics | Networks | Computer simulation | Mathematical models | Dynamical systems
Neurons | Bifurcation | Stability analysis | Mathematical model | Hopf bifurcation | neural network | Biological neural networks | Delay | Bidirectional associative memory | Neural network | EXISTENCE | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | EXPONENTIAL STABILITY | TIME-VARYING DELAYS | 2-NEURON SYSTEM | GLOBAL ASYMPTOTIC STABILITY | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | delay | DISTRIBUTED DELAYS | DYNAMICS | NEURONS | COMPUTER SCIENCE, THEORY & METHODS | Algorithms | Information Storage and Retrieval - methods | Time Factors | Feedback | Memory | Models, Neurological | Nonlinear Dynamics | Neural Networks (Computer) | Delay lines | Usage | Numerical analysis | Neural networks | Simulation methods | Innovations | Studies | Economic models | Nonlinear dynamics | Networks | Computer simulation | Mathematical models | Dynamical systems
Journal Article
International Journal of Forecasting, ISSN 0169-2070, 10/2006, Volume 22, Issue 4, pp. 637 - 666
In Gardner [Gardner, E. S., Jr. (1985). Exponential smoothing: The state of the art. Journal of Forecasting 4, 1–28], I reviewed the research in exponential...
Time series—ARIMA, exponential smoothing, state-space models, identification, stability, invertibility, model selection | Prediction intervals | Inventory control | Intermittent demand | Regression—discount weighted, kernel | Comparative methods—evaluation | Comparative methods-evaluation | Time series-ARIMA, exponential smoothing, state-space models, identification, stability, invertibility, model selection | Regression-discount weighted, kernel | LEAD-TIME DEMAND | MULTIPLICATIVE HOLT-WINTERS | comparative methods | inventory control | MANAGEMENT | intermittent demand | time series | MISSING OBSERVATIONS | prediction intervals | ARIMA, exponential smoothing, state-space models, identification, stability, invertibility, model selection | evaluation | regression | WEIGHTED MOVING AVERAGES | MODEL SELECTION | FORECASTING INTERMITTENT DEMAND | ECONOMICS | discount weighted, kernel | SERIES MODELS | IRREGULAR UPDATING PERIODS | Smoothing (Numerical analysis) | Usage | Forecasts and trends | Time-series analysis | Research | Analysis
Time series—ARIMA, exponential smoothing, state-space models, identification, stability, invertibility, model selection | Prediction intervals | Inventory control | Intermittent demand | Regression—discount weighted, kernel | Comparative methods—evaluation | Comparative methods-evaluation | Time series-ARIMA, exponential smoothing, state-space models, identification, stability, invertibility, model selection | Regression-discount weighted, kernel | LEAD-TIME DEMAND | MULTIPLICATIVE HOLT-WINTERS | comparative methods | inventory control | MANAGEMENT | intermittent demand | time series | MISSING OBSERVATIONS | prediction intervals | ARIMA, exponential smoothing, state-space models, identification, stability, invertibility, model selection | evaluation | regression | WEIGHTED MOVING AVERAGES | MODEL SELECTION | FORECASTING INTERMITTENT DEMAND | ECONOMICS | discount weighted, kernel | SERIES MODELS | IRREGULAR UPDATING PERIODS | Smoothing (Numerical analysis) | Usage | Forecasts and trends | Time-series analysis | Research | Analysis
Journal Article
Journal of Econometrics, ISSN 0304-4076, 12/2019, Volume 213, Issue 2, pp. 434 - 458
This paper develops a nondegenerate likelihood-ratio test for model selection between two competitive spatial econometrics models. It generalizes the test of...
Likelihood ratio | Matrix exponential spatial specification | Spatial autoregressive model | Near-epoch dependence | Model selection | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | AUTOREGRESSIVE MODELS | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | SPECIFICATION | Analysis | Econometric models | Models
Likelihood ratio | Matrix exponential spatial specification | Spatial autoregressive model | Near-epoch dependence | Model selection | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | AUTOREGRESSIVE MODELS | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | SPECIFICATION | Analysis | Econometric models | Models
Journal Article
Journal of the Royal Statistical Society. Series B (Statistical Methodology), ISSN 1369-7412, 1/2014, Volume 76, Issue 1, pp. 29 - 46
Models of dynamic networks—networks that evolve over time—have manifold applications. We develop a discrete time generative model for social network evolution...
Maximum likelihood estimation | Social networks | Exponential random‐graph model | Markov chain Monte Carlo methods | Longitudinal network | Exponential random-graph model | DISCRETE TEMPORAL MODELS | EXPONENTIAL FAMILY MODELS | STATISTICS & PROBABILITY | Markov processes | Monte Carlo method | Algorithms | Analysis | Studies | Graph theory | Dynamical systems | Networks | Dynamics | Evolution | Models | Flexibility | Temporal logic | Statistics - Methodology | Exponential random graph model | Longitudinal | Markov chain Monte Carlo
Maximum likelihood estimation | Social networks | Exponential random‐graph model | Markov chain Monte Carlo methods | Longitudinal network | Exponential random-graph model | DISCRETE TEMPORAL MODELS | EXPONENTIAL FAMILY MODELS | STATISTICS & PROBABILITY | Markov processes | Monte Carlo method | Algorithms | Analysis | Studies | Graph theory | Dynamical systems | Networks | Dynamics | Evolution | Models | Flexibility | Temporal logic | Statistics - Methodology | Exponential random graph model | Longitudinal | Markov chain Monte Carlo
Journal Article
The Annals of Statistics, ISSN 0090-5364, 6/2013, Volume 41, Issue 3, pp. 1085 - 1110
We study maximum likelihood estimation for the statistical model for undirected random graphs, known as the β-model, in which the degree sequences are minimal...
Polytopes | Maximum likelihood estimation | Statistical graphs | Statistical models | Nonexistence | Modeling | Parametric models | Probabilities | Vertices | Maximum likelihood estimator | Polytope of degree sequences | β-model | Random graphs | polytope of degree sequences | maximum likelihood estimator | PROBABILITY-DISTRIBUTIONS | DIRECTED-GRAPHS | beta-model | EXPONENTIAL-FAMILIES | EXPOTENTIAL FAMILY | STATISTICS & PROBABILITY | random graphs | 62F99
Polytopes | Maximum likelihood estimation | Statistical graphs | Statistical models | Nonexistence | Modeling | Parametric models | Probabilities | Vertices | Maximum likelihood estimator | Polytope of degree sequences | β-model | Random graphs | polytope of degree sequences | maximum likelihood estimator | PROBABILITY-DISTRIBUTIONS | DIRECTED-GRAPHS | beta-model | EXPONENTIAL-FAMILIES | EXPOTENTIAL FAMILY | STATISTICS & PROBABILITY | random graphs | 62F99
Journal Article
Journal of the American Statistical Association, ISSN 0162-1459, 07/2014, Volume 109, Issue 507, pp. 991 - 1007
Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and...
Bootstrap smoothing | ABC intervals | Lasso | Model averaging | Bagging | Importance sampling | REGRESSION | CONFIDENCE-INTERVALS | STATISTICS | C-p | STATISTICS & PROBABILITY | SUPERNOVAE | INFERENCE | JACKKNIFE | STANDARD ERRORS | BOOTSTRAP | EXPONENTIAL-FAMILIES | Regression analysis | Bootstrapping (Statistics) | Analysis | Nonparametric statistics | model averaging | importance sampling | bootstrap smoothing | bagging
Bootstrap smoothing | ABC intervals | Lasso | Model averaging | Bagging | Importance sampling | REGRESSION | CONFIDENCE-INTERVALS | STATISTICS | C-p | STATISTICS & PROBABILITY | SUPERNOVAE | INFERENCE | JACKKNIFE | STANDARD ERRORS | BOOTSTRAP | EXPONENTIAL-FAMILIES | Regression analysis | Bootstrapping (Statistics) | Analysis | Nonparametric statistics | model averaging | importance sampling | bootstrap smoothing | bagging
Journal Article
Scandinavian Journal of Statistics, ISSN 0303-6898, 09/2017, Volume 44, Issue 3, pp. 684 - 706
Random effects model can account for the lack of fitting a regression model and increase precision of estimating area‐level means. However, in case that the...
binomial‐beta model, conditional mean squared error, Fay–Herriot model, mixed model, natural exponential family with quadratic variance function, Poisson‐gamma model, small area estimation, uncertain random effect | binomial-beta model, conditional mean squared error, Fay–Herriot model, mixed model, natural exponential family with quadratic variance function, Poisson-gamma model, small area estimation, uncertain random effect | natural exponential family with quadratic variance function | MIXED MODELS | ERRORS | NATURAL EXPONENTIAL-FAMILIES | STATISTICS & PROBABILITY | mixed model | PREDICTION | Fay-Herriot model | uncertain random effect | QUADRATIC VARIANCE FUNCTIONS | Poisson-gamma model | conditional mean squared error | binomial-beta model | small area estimation | Economic models | Uncertainty | Statistical analysis | Computer simulation | Estimation | Studies | Risk assessment | Distribution | Mathematical models | Models | Bayesian analysis | Estimators | International
binomial‐beta model, conditional mean squared error, Fay–Herriot model, mixed model, natural exponential family with quadratic variance function, Poisson‐gamma model, small area estimation, uncertain random effect | binomial-beta model, conditional mean squared error, Fay–Herriot model, mixed model, natural exponential family with quadratic variance function, Poisson-gamma model, small area estimation, uncertain random effect | natural exponential family with quadratic variance function | MIXED MODELS | ERRORS | NATURAL EXPONENTIAL-FAMILIES | STATISTICS & PROBABILITY | mixed model | PREDICTION | Fay-Herriot model | uncertain random effect | QUADRATIC VARIANCE FUNCTIONS | Poisson-gamma model | conditional mean squared error | binomial-beta model | small area estimation | Economic models | Uncertainty | Statistical analysis | Computer simulation | Estimation | Studies | Risk assessment | Distribution | Mathematical models | Models | Bayesian analysis | Estimators | International
Journal Article
Mathematical Finance, ISSN 0960-1627, 04/2016, Volume 26, Issue 2, pp. 395 - 411
We propose to interpret distribution model risk as sensitivity of expected loss to changes in the risk factor distribution, and to measure the distribution...
maximum entropy principle | relative entropy | multiple priors | divergence preferences | convex integral functional | Bregman distance | f‐divergence | generalized exponential family | Divergence preferences | Convex integral functional | Generalized exponential family | Relative entropy | f-divergence | Maximum entropy principle | Multiple priors | MAXIMUM-ENTROPY | BUSINESS, FINANCE | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | Studies | Economic theory | Risk management
maximum entropy principle | relative entropy | multiple priors | divergence preferences | convex integral functional | Bregman distance | f‐divergence | generalized exponential family | Divergence preferences | Convex integral functional | Generalized exponential family | Relative entropy | f-divergence | Maximum entropy principle | Multiple priors | MAXIMUM-ENTROPY | BUSINESS, FINANCE | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | Studies | Economic theory | Risk management
Journal Article
Engineering Applications of Artificial Intelligence, ISSN 0952-1976, 11/2017, Volume 66, pp. 49 - 59
Over the past few decades, a large literature has evolved to forecast time series using various linear, nonlinear and hybrid linear–nonlinear models. Recently,...
Exponential smoothing | Time series forecasting | ANN | Hybrid model | ETS | DECOMPOSITION | STATE | OF-THE-ART | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | ENGINEERING, MULTIDISCIPLINARY | ARIMA | NEURAL-NETWORK | AUTOMATION & CONTROL SYSTEMS | Neural networks | Analysis | Models
Exponential smoothing | Time series forecasting | ANN | Hybrid model | ETS | DECOMPOSITION | STATE | OF-THE-ART | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | ENGINEERING, MULTIDISCIPLINARY | ARIMA | NEURAL-NETWORK | AUTOMATION & CONTROL SYSTEMS | Neural networks | Analysis | Models
Journal Article
IEEE Transactions on Fuzzy Systems, ISSN 1063-6706, 02/2014, Volume 22, Issue 1, pp. 153 - 163
In this paper, a sampled-data fuzzy controller is designed to stabilize a class of chaotic systems. A Takagi-Sugeno (T-S) fuzzy model is employed to represent...
Chaos | Fuzzy control | Symmetric matrices | Educational institutions | Stability analysis | Delays | Takagi-Sugeno (T-S) fuzzy model | Chaotic systems | exponential stability | sampled-data control | DESIGN | STABILIZATION | IDENTIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | H-INFINITY CONTROL | SYNCHRONIZATION | NONLINEAR-SYSTEMS | DISCRETE | FAULT-DETECTION | DELAY | Fuzzy logic | Chaos theory | Synthesis | Fuzzy set theory | Transaction processing | Fuzzy | Sampling
Chaos | Fuzzy control | Symmetric matrices | Educational institutions | Stability analysis | Delays | Takagi-Sugeno (T-S) fuzzy model | Chaotic systems | exponential stability | sampled-data control | DESIGN | STABILIZATION | IDENTIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | H-INFINITY CONTROL | SYNCHRONIZATION | NONLINEAR-SYSTEMS | DISCRETE | FAULT-DETECTION | DELAY | Fuzzy logic | Chaos theory | Synthesis | Fuzzy set theory | Transaction processing | Fuzzy | Sampling
Journal Article
Journal of Statistical Mechanics: Theory and Experiment, ISSN 1742-5468, 2014, Volume 2014, Issue 5, pp. P05007 - 20
The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically...
EXPONENTIAL FAMILY | PROBABILITY-DISTRIBUTIONS | DIRECTED-GRAPHS | MECHANICS | clustering techniques | STOCHASTIC BLOCKMODELS | random graphs | statistical inference | networks | PHYSICS, MATHEMATICAL | message-passing algorithms | PREDICTION | Networks | Approximation | Communities | Blocking | Graphs | Mathematical models | Stochasticity | Standards
EXPONENTIAL FAMILY | PROBABILITY-DISTRIBUTIONS | DIRECTED-GRAPHS | MECHANICS | clustering techniques | STOCHASTIC BLOCKMODELS | random graphs | statistical inference | networks | PHYSICS, MATHEMATICAL | message-passing algorithms | PREDICTION | Networks | Approximation | Communities | Blocking | Graphs | Mathematical models | Stochasticity | Standards
Journal Article
Social Networks, ISSN 0378-8733, 05/2013, Volume 35, Issue 2, pp. 211 - 222
In this paper, we review the development of dependence structures for exponential random graph models for bipartite networks, and propose a hierarchy of...
Exponential random graph models | Bipartite networks | Edge-cycles | Dependence hierarchy | P-ASTERISK MODELS | ANTHROPOLOGY | SOCIOLOGY
Exponential random graph models | Bipartite networks | Edge-cycles | Dependence hierarchy | P-ASTERISK MODELS | ANTHROPOLOGY | SOCIOLOGY
Journal Article
Annual Review of Sociology, ISSN 0360-0572, 1/2011, Volume 37, Issue 1, pp. 131 - 153
Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity,...
Economic models | Statistical models | Theory and Methods | Social networking | Inference | Markov chains | Markov models | Modeling | Statistics | Parametric models | Probabilities | Social networks | Statistical modeling | inference | SOCIOMATRICES | social networks | STOCHASTIC BLOCKMODELS | RANDOM GRAPH MODELS | P-ASTERISK MODELS | DYADIC DATA | MARKOV GRAPHS | DISTRIBUTIONS | EXPONENTIAL FAMILY | DIRECTED-GRAPHS | DYNAMICS | statistical modeling | SOCIOLOGY | Models | Research | Sociological research | Analysis | Mathematical models | Statistical analysis | Markov analysis | Sociology
Economic models | Statistical models | Theory and Methods | Social networking | Inference | Markov chains | Markov models | Modeling | Statistics | Parametric models | Probabilities | Social networks | Statistical modeling | inference | SOCIOMATRICES | social networks | STOCHASTIC BLOCKMODELS | RANDOM GRAPH MODELS | P-ASTERISK MODELS | DYADIC DATA | MARKOV GRAPHS | DISTRIBUTIONS | EXPONENTIAL FAMILY | DIRECTED-GRAPHS | DYNAMICS | statistical modeling | SOCIOLOGY | Models | Research | Sociological research | Analysis | Mathematical models | Statistical analysis | Markov analysis | Sociology
Journal Article
1998, Lecture notes in statistics, ISBN 9813083298, Volume 130., ix, 230
Book
Social Networks, ISSN 0378-8733, 2009, Volume 31, Issue 1, pp. 12 - 25
Recent advances in Exponential Random Graph Models (ERGMs), or models, include new specifications that give a much better chance of model convergence for large...
MCMC MLE | Partial conditional dependence assumption | Exponential random graph ( [formula omitted]) models | Affiliation networks | Exponential random graph (p ) models | LOGIT-MODELS | Exponential random graph (p) models | LOGISTIC REGRESSIONS | SOCIAL NETWORKS | ANTHROPOLOGY | MARKOV GRAPHS | FAMILY MODELS | SOCIOLOGY | Markov processes | Analysis | Models
MCMC MLE | Partial conditional dependence assumption | Exponential random graph ( [formula omitted]) models | Affiliation networks | Exponential random graph (p ) models | LOGIT-MODELS | Exponential random graph (p) models | LOGISTIC REGRESSIONS | SOCIAL NETWORKS | ANTHROPOLOGY | MARKOV GRAPHS | FAMILY MODELS | SOCIOLOGY | Markov processes | Analysis | Models
Journal Article
Ecology, ISSN 0012-9658, 11/2007, Volume 88, Issue 11, pp. 2766 - 2772
Quasi-Poisson and negative binomial regression models have equal numbers of parameters, and either could be used for overdispersed count data. While they often...
Datasets | Statistical Reports | Statistical variance | Ecological modeling | Marine ecology | Linear regression | Binomial distributions | Seals | Regression analysis | Binomials | Marine mammals | generalized linear models | covariates | quasi models | iteratively weighted least squares | harbor seals | overdispersion | Iteratively weighted least squares | Overdispersion | Quasi models | Covariates | Generalized linear models | Harbor seals | PRINCE-WILLIAM-SOUND | DOUBLE-EXPONENTIAL-FAMILIES | ECOLOGY | ALASKA | ABUNDANCE | Binomial Distribution | Data Interpretation, Statistical | Population Growth | Probability | Linear Models | Models, Statistical | Data Collection - methods | Regression Analysis | Animals | Time Factors | Population Density | Phoca - growth & development | Poisson Distribution | Seasons | Harbor seal | Distribution | Least squares | Poisson distribution | Comparative analysis | Methods
Datasets | Statistical Reports | Statistical variance | Ecological modeling | Marine ecology | Linear regression | Binomial distributions | Seals | Regression analysis | Binomials | Marine mammals | generalized linear models | covariates | quasi models | iteratively weighted least squares | harbor seals | overdispersion | Iteratively weighted least squares | Overdispersion | Quasi models | Covariates | Generalized linear models | Harbor seals | PRINCE-WILLIAM-SOUND | DOUBLE-EXPONENTIAL-FAMILIES | ECOLOGY | ALASKA | ABUNDANCE | Binomial Distribution | Data Interpretation, Statistical | Population Growth | Probability | Linear Models | Models, Statistical | Data Collection - methods | Regression Analysis | Animals | Time Factors | Population Density | Phoca - growth & development | Poisson Distribution | Seasons | Harbor seal | Distribution | Least squares | Poisson distribution | Comparative analysis | Methods
Journal Article