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  1. Stochastic Processes with R: An Introduction
  2. Essentials of Econometrics
  3. Time Series for Data Sciences: Analysis and Forecasting
  4. Introduction to Statistics and Data Analysis (with Exercises, Solutions and Applications in R)
  5. Dynamic space–time panel data models: An eigendecomposition-based bias-corrected least squares procedure
  6. Estimation of Reliability in Multicomponent Set-up when Stress and Strength are Non-identical
  7. Robust dynamic space–time panel data models using $$\varepsilon $$-contamination: an application to crop yields and climate change
  8. Handbook of Regression Analysis with Applications in R (Second Edition)
  9. Modeling Structural Breaks in Disturbances Precision or Autoregressive Parameter in Dynamic Model: A Bayesian Approach
  10. Generalized Bayes Estimator for Spatial Durbin Model
  11. Finite sample performance of an estimator of process capability index Cpm for the autocorrelated data
  12. Robust estimation with variational Bayes in presence of competing risks
  13. Forest Cover-Type Prediction Using Model Averaging
  14. Bayesian Estimation and Unit Root Test for Logistic Smooth Transition Autoregressive Process
  15. Robust Bayesian analysis of a multivariate dynamic model
  16. GENERALIZED BAYES ESTIMATION OF SPATIAL AUTOREGRESSIVE MODELS
  17. Robust linear static panel data models usingε-contamination
  18. Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms
  19. Mining SNPs in extracellular vesicular transcriptome ofTrypanosoma cruzi: a step closer to early diagnosis of neglected Chagas disease
  20. Analysis of Panel Data
  21. Bayesian Inference for State Space Model with Panel Data
  22. Statistical process control for autocorrelated data on grid
  23. Bayesian analysis of a linear model involving structural changes in either regression parameters or disturbances precision
  24. Shrinkage estimation in spatial autoregressive model
  25. Bayesian Analysis of Structural Changes in a Linear Regression Model: An Application to Rupee-Dollar Exchange Rate
  26. Modeling Count Data J. M. Hilbe Cambridge Cambridge University Press xvi + 284 pp., $99.00 (hardbound), $37.99 (paperbound) ISBN 978-1-107-02833-3 (hardbound), 978-1-107-61125-2 (paperbound)
  27. Cross-Family Comparative Proteomic Study and Molecular Phylogeny of MAP Kinases in Plants
  28. Cross family comparative proteomic study and molecular phylogeny of MAP kinases in plants
  29. Bayesian Estimation of Regression Coefficients Under Extended Balanced Loss Function
  30. Robust Bayesian analysis of Weibull failure model
  31. Estimation of a subset of regression coefficients of interest in a model with non-spherical disturbances
  32. Mining and gene ontology based annotation of SSR markers from expressed sequence tags of Humulus lupulus
  33. Confidence ellipsoids based on a general family of shrinkage estimators for a linear model with non-spherical disturbances
  34. Effect of Misspecifying the Disturbance Covariance Matrix on a Family of Shrinkage Estimators
  35. Simultaneous Prediction Based on Shrinkage Estimator
  36. Bayesian Unit Root Test for Time Series Models with Structural Breaks
  37. Bayesian unit root test for model with maintained trend
  38. Appendix: Performance of the 2SHI Estimator Under the Generalised Pitman Nearness Criterion
  39. Risk and Pitman closeness properties of feasible generalized double k-class estimators in linear regression models with non-spherical disturbances under balanced loss function
  40. Unbiased estimation of the MSE matrices of improved estimators in linear regression
  41. Improved Multivariate Prediction in a General Linear Model with an Unknown Error Covariance Matrix
  42. Handbook Of Applied Econometrics And Statistical Inference
  43. Double k-Class Estimators in Regression Models with Non-spherical Disturbances
  44. STEIN-RULE RESTRICTED REGRESSION ESTIMATOR IN A LINEAR REGRESSION MODEL WITH NONSPHERICAL DISTURBANCES
  45. Bayesian analysis of disturbances variance in the linear regression model under asymmetric loss functions
  46. Exact Results on the Inadmissibility of the Feasible Generalized Least Squares Estimator in Regression Models with Non-Spherical Disturbances
  47. Stein rule prediction of the composite target function in a general linear regression model
  48. Operational Variants of the Minimum Mean Squared Error Estimator in Linear Regression Models with Non-Spherical Disturbances
  49. Bayesian Unit Root Test in Nonnormal AR(1) Model
  50. Bayesian estimation for the Pareto income distribution
  51. BAYESIAN ANALYSIS OF THE LINEAR REGRESSION MODEL WITH NON-NORMAL DISTURBANCES
  52. Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances
  53. Bayesian analysis of the linear regression model with an edgeworth series prior distribution
  54. Performance of the 2SHI estimator under the generalised pitman nearness criterion
  55. Robust Bayesian analysis of the linear regression model
  56. Bayesian predictive analysis of the linear regression model with an edgeworth series prior distribution
  57. Selecting a double k-class estimator for regression coefficients
  58. Asymptotic approximations to the gain of the 2shi over stein estimators in linear regression models when the disturbances are small
  59. Ridge regression estimators in the linear regression models with non-spherical errors
  60. On two Sequential Procedures for Estimating the Parameter of a Uniform Distribution
  61. Lindley-like mean correction in the improved estimation of regression models with non-scalar covariance matrix
  62. Comparison of improved regression estimators with and without moments
  63. The necessary and sufficient conditions for the uniform dominance of the two-stage stein estimators
  64. A necessary and sufficient condition for the dominance of an improved family of estimators in linear regression models
  65. Some properties of the distribution of an operational ridge estimator
  66. Estimation of Linear Regression Model with Random Coefficients Ensuring Almost Non‐Negativity of Variance Estimators