The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametr…
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48 results for “Maximum Parsimony”
Proposes a new approach to approximate maximum likelihood for complex models.
problem Intractable likelihood functions in complex parametric models.
method Simulation-based constrained approximation to the structural model.
result Estimators nearly as efficient as maximum likelihood, feasible in many cases.
Develops method for learning complex nonlinear systems with multiple outputs and inputs.
problem Learning nonlinear systems with multiple outputs and inputs.
method Latent variable framework, maximum likelihood principle, majorization-minimization approach, convex majorization technique.
result Recursive identification of parsimonious predictive models.
Parsimonious Bayesian deep networks infer optimal architectures from data.
problem Optimizing deep network architectures for efficiency and accuracy.
method Combining Bayesian nonparametrics with a greedy layer-wise learning algorithm.
result Achieves state-of-the-art classification accuracy with low computational complexity.
Optimizes a portfolio with mean-reverting assets using Ornstein-Uhlenbeck process.
problem Design a portfolio with high mean reversion and low variance.
method Penalized OU-Likelihood Estimation with specialized algorithm.
result Parsimonious portfolio selection with desirable characteristics.
Stochastic Kronecker graphs supply a parsimonious model for large sparse real world graphs. They can specify the distribution of a large random graph using only three or four parameters. Those parameters have however proved difficult to choose in specific applications. This article looks at method of moments estimators…
Parsimonious neural networks discover interpretable physical laws from data.
problem Discovering interpretable physical laws from data using machine learning.
method Combining neural networks with evolutionary optimization to balance accuracy and parsimony.
result Developed models for classical mechanics and materials melting temperature prediction.
New semimetrics maximize test power for comparing distributions.
problem Comparing and understanding differences between distributions.
method Features optimized to maximize test power, converging with sample size.
result Linear-time tests achieve comparable performance to state-of-the-art methods.
In this paper we examine the claims reserving problem using Tweedie's compound Poisson model. We develop the maximum likelihood and Bayesian Markov chain Monte Carlo simulation approaches to fit the model and then compare the estimated models under different scenarios. The key point we demonstrate relates to the compar…
We study modeling and inference with the Elliptical Gamma Distribution (EGD). We consider maximum likelihood (ML) estimation for EGD scatter matrices, a task for which we develop new fixed-point algorithms. Our algorithms are efficient and converge to global optima despite nonconvexity. Moreover, they turn out to be mu…
This paper proposes a parsimoniously time varying parameter vector autoregressive model (with exogenous variables, VARX) and studies the properties of the Lasso and adaptive Lasso as estimators of this model. The parameters of the model are assumed to follow parsimonious random walks, where parsimony stems from the ass…
This paper models cryptocurrencies using -stable distributions, outperforming other models.
problem Modeling the highly speculative and leptokurtic nature of cryptocurrencies.
method Used -stable distribution and compared it with other heavy tailed distributions. Employed maximum likelihood method for estimation.
result The -stable distribution fits cryptocurrency return data better than other models.
A parsimonious model reduces over-parameterization in skewed matrix variate mixtures.
problem Over-parameterization in skewed matrix variate mixtures.
method Parsimonious family of 256 models using bilinear factor analyzers constrained over clusters, with AECM algorithm for estimation.
result Extensive simulations and real-world datasets (MNIST, Olivetti faces) demonstrate the method's effectiveness.
Simple model prices swaptions in multicurve interest rates.
problem Pricing swaptions in multicurve interest rate models.
method Three-parameter multicurve extension of Hull-White model.
result Simple closed formula for swaption pricing.
A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An…
A family of parsimonious Gaussian cluster-weighted models is presented. This family concerns a multivariate extension to cluster-weighted modelling that can account for correlations between multivariate responses. Parsimony is attained by constraining parts of an eigen-decomposition imposed on the component covariance …
Copula models for sovereign ratings improved by incorporating climate risk.
problem Modeling nonlinear dependence and clustering in sovereign rating migrations.
method Mixed-difference transformation, MAGMAR(1,1) copula process, consistent and asymptotically normal estimators.
result Gumbel MAGMAR(1,1) specification outperforms other models in empirical performance.
Proposes a parsimonious graph spectral method for time series data.
problem Efficiently transmitting multivariate time series data.
method Graph spectral embedding with unsupervised, parsimonious encoding.
result Near-linear computational complexity and interpretable event structure.
The expectation-maximization (EM) algorithm is an iterative method for finding maximum likelihood estimates when data are incomplete or are treated as being incomplete. The EM algorithm and its variants are commonly used for parameter estimation in applications of mixture models for clustering and classification. This …
Develops a method to infer partial rankings from sparse comparisons.
problem Challenges in ranking items with limited and noisy comparisons.
method Nonparametric Bayesian approach for learning partial rankings.
result Finds partial rankings that distinguish meaningful differences only when data supports it.
Astrophysics uses phylogenetic methods to classify galaxies, overcoming limitations of traditional Hubble classification.
problem Classifying distant galaxies and understanding their evolutionary relationships.
method Maximum Parsimony and cladistics applied to multivariate astrophysical data.
result Maximum Parsimony provides useful astrophysical results for large datasets of galaxies.
Phylogenetic Tools in Astrophysicsastro-ph.IM
Astrophysics uses phylogenetic tools to classify evolving objects.
problem Classifying evolving astrophysical entities like galaxies and clusters.
method Phylogenetic approach, addressing challenges of hierarchical diversity and continuous parameters.
result Conceptual and practical difficulties have been solved with limited samples.
Explosive neural networks via higher-order interactions in curved statistical manifoldscond-mat.dis-nn
New neural networks model complex phenomena with fewer parameters.
problem Challenges in studying higher-order interactions in neural networks.
method Introducing curved neural networks using the maximum entropy principle.
result Curved neural networks accelerate memory retrieval and exhibit explosive phase transitions.
Finite mixtures of regression models offer a flexible framework for investigating heterogeneity in data with functional dependencies. These models can be conveniently used for unsupervised learning on data with clear regression relationships. We extend such models by imposing an eigen-decomposition on the multivariate …
Parsimonious module inference in large networksphysics.data-an
We investigate the detectability of modules in large networks when the number of modules is not known in advance. We employ the minimum description length (MDL) principle which seeks to minimize the total amount of information required to describe the network, and avoid overfitting. According to this criterion, we obta…
New GMM models fit high-dimensional data with fewer parameters.
problem Overparameterization and lack of flexibility in GMMs for high-dimensional data.
method Piecewise-constant covariance eigenvalue profiles, EM and penalized EM algorithms.
result Superior likelihood-parsimony tradeoffs in density fitting, clustering, and denoising.
Bayesian context trees capture complex dependencies in categorical sequences.
problem Complex, long-range dependencies in categorical sequences are not well captured by simple models.
method Parsimonious Bayesian context trees with model-based agglomerative clustering for efficient inference.
result The proposed framework outperforms existing models on real-world data.
Proposes a non-convex optimization method for a parsimonious weighted naive Bayes classifier.
problem Improving naïve Bayes classifier performance with a large number of input variables.
method Sparse regularization of model log-likelihood for direct estimation of variable weights.
result Optimization-based weighted naïve Bayes classifiers achieve equivalent performance to averaging-based classifiers.
Investigates deep hedging under rough volatility models.
problem Performance of deep hedging framework under non-Markovian conditions.
method Analysis of rough volatility models, use of parsimonious network architectures.
result Parsimonious network architectures can capture non-Markovian time-series.
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning frameworkcs.LG
New algorithms improve ensemble diversity, leading to more accurate and smaller models.
problem Building accurate predictive models with diverse base predictors.
method Integrates ensemble diversity into a reinforcement learning framework for ensemble selection.
result Diversity-incorporating ensembles are more accurate and smaller in size.
A new method limits over-clustering in DPM models.
problem Over-clustering in Dirichlet process mixture models.
method Powered Chinese restaurant process to penalize over-clustering.
result Reduces the number of clusters in large datasets.
A new method reduces high-dimensional data's impact on CWMs using TSNE.
problem High-dimensional data hampers CWMs' accuracy and speed.
method TSNE for dimensionality reduction, parsimonious technique, expectation maximization.
result TSNE enhances CWMs' performance in high-dimensional space.
Method selects most useful network model for various tasks.
problem Impact of translating raw data to network models is unexamined.
method Proposes a network model selection methodology focusing on utility and parsimony.
result Demonstrates the importance of network definition for system behavior.
Proposes a new method for non-parametric regression with adaptive truncation.
problem Sparse high-dimensional additive models with varying complexity.
method Convex hierarchical penalty combining finite basis and smoothing penalties.
result Converges at minimax rate for hierarchical function classes and sparse additive models.
Parsimonious Dynamic Mode Decomposition selects sparse modes robustly.
problem Manual tuning of sparsity parameters in traditional DMD.
method Time-delay embedding and Orthogonal Matching Pursuit.
result Autonomously determines optimally sparse subset of modes.
A new tensor regression model preserves multidimensional data structure.
problem Complex multidimensional data loses intrinsic connections and parameter explosion.
method Developed a parsimonious tensor regression model using Tucker structure and shrinkage penalization.
result The model outperforms benchmark models in forecasting.
A new method for efficient ordinal regression.
problem Efficiently modeling ordinal relationships in multi-label learning.
method Incremental Sparse Bayesian Ordinal Regression (ISBOR) approach.
result ISBOR achieves accurate predictions with fewer basis functions.
A new co-clustering model for high-dimensional data reduces parameter complexity.
problem High-dimensional data challenges traditional co-clustering methods.
method Parameter-wise co-clustering model with SEM and Gibbs sampler for estimation.
result The model maintains parsimony while offering more flexibility.
Optimal AFs minimize RFR test error and sensitivity.
problem Finding optimal AFs for RFR to minimize test error and sensitivity.
method Closed-form solution for AFs minimizing test error and sensitivity under different functional parsimony.
result Optimal AFs can be linear, saturated linear, or Hermite polynomial expressions.
Efficiently models financial data with parsimonious sparse structures.
problem Estimating sparse inverse covariance matrices for financial data.
method Information Filtering Networks for local and parallelizable computations.
result Computational efficiency and robustness in sparse inference.
We present a comprehensive theory of homogeneous volatility (and variance) estimators of arbitrary stochastic processes that fully exploit the OHLC (open, high, low, close) prices. For this, we develop the theory of most efficient point-wise homogeneous OHLC volatility estimators, valid for any price processes. We intr…
Complex knot invariants are computationally hard.
problem Computing homomorphisms from knot groups to nonabelian simple groups.
method Using braid group actions to count homomorphisms from knot groups to nonabelian simple groups.
result Counting homomorphisms is almost parsimoniously -complete.
Novel estimation methods improve MAR model accuracy for high-dimensional time series.
problem Limited estimation techniques for Matrix Autoregressive (MAR) models.
method Adapted Yule-Walker equations and Burg's method.
result Proposed methods achieve comparable model fit to VAR models.
Bayesian method reconstructs hidden higher-order interactions from network data.
problem Lack of explicit higher-order interactions in pairwise network data.
method Bayesian approach based on parsimony, infers higher-order structures when statistically supported.
result Demonstrated applicability to various datasets, synthetic and empirical.
The evolution of personal income distribution (PID) in four countries: Canada, New Zealand, the UK, and the USA follows a unique trajectory. We have revealed precise match in the shape of two age-dependent features of the PID: mean income and the portion of people with the highest incomes (2 to 5% of the working age po…
Recent years have demonstrated that using random feature maps can significantly decrease the training and testing times of kernel-based algorithms without significantly lowering their accuracy. Regrettably, because random features are target-agnostic, typically thousands of such features are necessary to achieve accept…
Researchers develop multi-utility representations for incomplete preferences linked to risk measures.
problem Handling incomplete preferences induced by set-valued risk measures.
method Established dual representations of set-valued risk measures to create parsimonious and well-behaved multi-utility representations.
result Unified dual representations of set-valued risk measures, linking them to scalar risk measures.
For a long time interest-rate models were built on a single yield curve used both for discounting and forwarding. However, the crisis that has affected financial markets in the last years led market players to revise this assumption and accommodate basis-swap spreads, whose remarkable widening can no longer be neglecte…