All Stories

  1. Designing unsupervised mixed‐type feature selection techniques using the heterogeneous correlation matrix
  2. Heckman selection-contaminated normal model
  3. Handling skewness and directional tails in model-based clustering
  4. Discrete mode-mixtures of unimodal positive distributions with an application to solar energy in South Africa
  5. LRDP: an R package implementing a new class of decompositions for orthogonal matrices
  6. Finite mixtures of multivariate skew tail-inflated normal distributions
  7. An EM algorithm for fitting matrix-variate normal distributions on interval-censored and missing data
  8. On the Number of Components for Matrix‐Variate Mixtures: A Comparison Among Information Criteria
  9. Hidden semi-Markov models for rainfall-related insurance claims
  10. A refreshing take on the inverted Dirichlet via a mode parameterization with some statistical illustrations
  11. Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns
  12. A New Look at the Dirichlet Distribution: Robustness, Clustering, and Both Together
  13. Mode mixture of unimodal distributions for insurance loss data
  14. Women and insurance pricing policies: a gender-based analysis with GAMLSS on two actuarial datasets
  15. The generalized hyperbolic family and automatic model selection through the multiple‐choiceLASSO
  16. Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions
  17. Parsimonious mixtures for the analysis of tensor-variate data
  18. Model-based clustering using a new multivariate skew distribution
  19. Matrix-Variate Hidden Markov Regression Models: Fixed and Random Covariates
  20. On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19
  21. Model-based clustering via skewed matrix-variate cluster-weighted models
  22. Parsimonious hidden Markov models for matrix-variate longitudinal data
  23. Dimension-wise scaled normal mixtures with application to finance and biometry
  24. Assessing Measurement Invariance for Longitudinal Data through Latent Markov Models
  25. Mixtures of Matrix-Variate Contaminated Normal Distributions
  26. Multiple scaled symmetric distributions in allometric studies
  27. Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies
  28. Two new matrix-variate distributions with application in model-based clustering
  29. Unconstrained representation of orthogonal matrices with application to common principal components
  30. The multivariate tail-inflated normal distribution and its application in finance
  31. Dichotomous unimodal compound models: application to the distribution of insurance losses
  32. Leptokurtic moment-parameterized elliptically contoured distributions with application to financial stock returns
  33. Robust model-based clustering with mild and gross outliers
  34. Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions
  35. High-dimensional unsupervised classification via parsimonious contaminated mixtures
  36. A Random-covariate Approach for Distal Outcome Prediction with Latent Class Analysis
  37. Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition
  38. A new look at the inverse Gaussian distribution with applications to insurance and economic data
  39. Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population
  40. On the Use of the Sub-Gaussian $$\alpha $$α-Stable Distribution in the Cluster-Weighted Model
  41. Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions
  42. Mixtures of multivariate contaminated normal regression models
  43. Compound unimodal distributions for insurance losses
  44. Dealing with omitted answers in a survey on social integration of immigrants in Italy
  45. Testing for serial independence
  46. The multivariate leptokurtic-normal distribution and its application in model-based clustering
  47. A diagram to detect serial dependencies: an application to transport time series
  48. Clustering Multivariate Longitudinal Observations: The Contaminated Gaussian Hidden Markov Model
  49. Multilevel cluster-weighted models for the evaluation of hospitals
  50. Parsimonious mixtures of multivariate contaminated normal distributions
  51. Spatial attraction in migrants' settlement patterns in the city of Catania
  52. Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers
  53. Decision boundaries for mixtures of regressions
  54. The Kullback–Leibler autodependogram
  55. Hypothesis Testing for Mixture Model Selection
  56. A time-dependent extension of the projected normal regression model for longitudinal circular data based on a hidden Markov heterogeneity structure
  57. Clustering bivariate mixed-type data via the cluster-weighted model
  58. Erratum to: The Generalized Linear Mixed Cluster-Weighted Model
  59. The Generalized Linear Mixed Cluster-Weighted Model
  60. Cluster-weighted $$t$$ t -factor analyzers for robust model-based clustering and dimension reduction
  61. SDD : An R Package for Serial Dependence Diagrams
  62. Parsimonious Generalized Linear Gaussian Cluster-Weighted Models
  63. Bivariate discrete beta Kernel graduation of mortality data
  64. On the Upward Bias of the Dissimilarity Index and Its Corrections
  65. Flexible mixture modelling with the polynomial Gaussian cluster-weighted model
  66. Model-based clustering via linear cluster-weighted models
  67. Refusal to Answer Specific Questions in a Survey: A Case Study
  68. KernSmoothIRT : An R Package for Kernel Smoothing in Item Response Theory
  69. DBKGrad : An R Package for Mortality Rates Graduation by Discrete Beta Kernel Techniques
  70. Estimating a Rasch Model via Fuzzy Empirical Probability Functions
  71. On the Spectral Decomposition in Normal Discriminant Analysis
  72. Closed Likelihood Ratio Testing Procedures to Assess Similarity of Covariance Matrices
  73. Clustering and classification via cluster-weighted factor analyzers
  74. Testing Serial Independence via Density-Based Measures of Divergence
  75. Detecting serial dependencies with the reproducibility probability autodependogram
  76. Using the Autodependogram in Model Diagnostic Checking
  77. Using the Variation Coefficient for Adaptive Discrete Beta Kernel Graduation
  78. Finite mixtures of unimodal beta and gamma densities and the $$k$$ -bumps algorithm
  79. Graduation by Adaptive Discrete Beta Kernels
  80. Checking Serial Independence of Residuals from a Nonlinear Model
  81. The autodependogram: a graphical device to investigate serial dependences
  82. Discrete approximations of continuous and mixed measures on a compact interval
  83. Assessing the pattern of covariance matrices via an augmentation multiple testing procedure
  84. Discrete Beta Kernel Graduation of Age-Specific Demographic Indicators
  85. Discrete Beta-Type Models
  86. Considerations on the Impact of Ill-Conditioned Configurations in the CML Approach