All Stories

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