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2007

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Model-based methods to identify multiple cluster structures in a data set

13 years 4 months ago

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Model-based clustering exploits finite mixture models for detecting group in a data set. It provides a sound statistical framework which can address some important issues, such as...

Giuliano Galimberti, Gabriele Soffritti

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Id	Views	Title	Status

1	264	Model-based methods to identify multiple cluster structures in a data set	claim
2	202	Bayesian estimation of the Gaussian mixture GARCH model	claim
3	172	AMCMC: An R interface for adaptive MCMC	claim
4	169	A null space method for over-complete blind source separation	claim
5	159	Multivariate out-of-sample tests for Granger causality	claim
6	152	Robust variable selection using least angle regression and elemental set sampling	claim
7	151	Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments	claim
8	148	Classifying densities using functional regression trees: Applications in oceanology	claim
9	142	DIVCLUS-T: A monothetic divisive hierarchical clustering method	claim
10	137	Fitting finite mixtures of generalized linear regressions in R	claim
11	136	A note on iterative marginal optimization: a simple algorithm for maximum rank correlation estimation	claim
12	134	Variational approximations in Bayesian model selection for finite mixture distributions	claim
13	131	Bivariate density estimation using BV regularisation	claim
14	128	Computational methods for case-cohort studies	claim
15	128	Regularized linear and kernel redundancy analysis	claim
16	127	Absorption of shocks in nonlinear autoregressive models	claim
17	127	Computing the least quartile difference estimator in the plane	claim
18	126	A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities	claim
19	124	Wavelet based time-varying vector autoregressive modelling	claim
20	123	Bayesian estimation of unrestricted and order-restricted association models for a two-way contingency table	claim
21	120	Boosting ridge regression	claim
22	117	Smoothing splines estimators in functional linear regression with errors-in-variables	claim
23	116	Exploring the state sequence space for hidden Markov and semi-Markov chains	claim
24	115	Arbitrarily shaped multiple spatial cluster detection for case event data	claim
25	115	Quasi-Monte Carlo estimation in generalized linear mixed models	claim
26	115	Algorithms for compact letter displays: Comparison and evaluation	claim
27	114	Relaxed Lasso	claim
28	111	On rank tests for shift detection in time series	claim
29	110	Two-way imputation: A Bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximation	claim
30	110	Bayesian modelling strategies for spatially varying regression coefficients: A multivariate perspective for multiple outcomes	claim
31	109	Improving the computation of censored quantile regressions	claim
32	108	Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm	claim
33	108	Diagnostics for functional regression via residual processes	claim
34	106	Parsimonious additive models	claim
35	105	Calculation of simplicial depth estimators for polynomial regression with applications	claim
36	105	Joint segmentation of wind speed and direction using a hierarchical model	claim
37	105	Cobra: A package for co-breaking analysis	claim
38	105	GSA-based maximum likelihood estimation for threshold vector error correction model	claim
39	105	Model selection for support vector machines via uniform design	claim
40	104	Archimedean copula estimation using Bayesian splines smoothing techniques	claim
41	104	Use of SVD-based probit transformation in clustering gene expression profiles	claim
42	103	Cluster-wise assessment of cluster stability	claim
43	102	Smooth functions and local extreme values	claim
44	102	On the robust detection of edges in time series filtering	claim
45	101	The evaluation of evidence for exponentially distributed data	claim
46	100	Estimation of fractional integration in the presence of data noise	claim
47	100	Convergence of random k-nearest-neighbour imputation	claim
48	100	Building a robust linear model with forward selection and stepwise procedures	claim
49	100	Input selection and shrinkage in multiresponse linear regression	claim
50	100	Estimation in a linear multivariate measurement error model with a change point in the data	claim
51	99	CLUES: A non-parametric clustering method based on local shrinking	claim
52	98	A new Bayes estimate of the change point in the hazard function	claim
53	97	Advances in Mixture Models	claim
54	96	On a quadratic eigenproblem occurring in regularized total least squares	claim
55	94	Some extensions of score matching	claim
56	92	Improving the reliability of bootstrap tests with the fast double bootstrap	claim
57	92	The Bayes factor for inequality and about equality constrained models	claim
58	92	On the estimation of the linear relation when the error variances are known	claim
59	91	Unbiased split selection for classification trees based on the Gini Index	claim
60	90	Robust forecasting of mortality and fertility rates: A functional data approach	claim
61	88	A study of partial F tests for multiple linear regression models	claim
62	88	Depth-based inference for functional data	claim
63	87	Estimation and inference in functional mixed-effects models	claim
64	86	A convenient way of generating gamma random variables using generalized exponential distribution	claim
65	85	Markov models for digraph panel data: Monte Carlo-based derivative estimation	claim
66	85	A semi-parametric approach for mixture models: Application to local false discovery rate estimation	claim
67	85	A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models	claim
68	84	Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes	claim
69	82	Estimation of the income distribution and detection of subpopulations: An explanatory model	claim
70	82	On hybrid methods of inverse regression-based algorithms	claim
71	82	Non-parametric log-concave mixtures	claim
72	81	A stochastic EM algorithm for a semiparametric mixture model	claim
73	80	A bootstrap approach to test the conditional symmetry in time series models	claim
74	79	Modelling series of studies with a common structure	claim
75	76	Independent component analysis based on symmetrised scatter matrices	claim
76	76	Robust learning from bites for data mining	claim
77	76	Applications of TLS and related methods in the environmental sciences	claim
78	75	Level choice in truncated total least squares	claim
79	75	Robust counterparts of errors-in-variables problems	claim
80	74	Inference via kernel smoothing of bootstrap P values	claim
81	74	Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter	claim
82	71	Fast robust regression algorithms for problems with Toeplitz structure	claim
83	69	Discovery, visualization and performance analysis of enterprise workflow	claim
84	69	Prediction intervals for regression models	claim
85	69	Fast cross-validation of high-breakdown resampling methods for PCA	claim
86	69	Algorithms and applications for approximate nonnegative matrix factorization	claim
87	68	Kernel ellipsoidal trimming	claim
88	67	On sovereign credit migration: A study of alternative estimators and rating dynamics	claim
89	67	A robust estimator for the tail index of Pareto-type distributions	claim
90	66	Knot selection by boosting techniques	claim
91	65	Multivariate mixed normal conditional heteroskedasticity	claim
92	62	Semiparametric regression for assessing agreement using tolerance bands	claim
93	58	A unifying model involving a categorical and/or dimensional reduction for multimode data	claim
94	49	Period analysis of variable stars: Temporal dependence and local optima	claim

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