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Book(print)#21952
Advanced statistical methods in biometric research
In: Wiley publications in statistics
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Linear models and generalizations: least squares and alternatives
In: Springer series in statistics
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Linear models: least squares and alternatives
In: Springer series in statistics
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Financial, macro and micro econometrics using R
In: Handbook of statistics volume 42
Part I. Finance -- 1. Financial econometrics and big data: a survey of volatility estimators and tests for the presence of jumps and co-jumps / Arpita Mukherjee, Weijia Peng, Norman R. Swanson, Xiye Yang -- 2. Real time monitoring of asset markets: bubbles and crises / Peter C.B. Phillips, Shuping Shi -- 3. Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction / Jianghao Chu, Tae-Hwy Lee, Aman Ullah -- Part II. Macro Econometrics -- 4. Mixed data sampling (MIDAS) regression models / Eric Ghysels, Virmantas Kvedaras, Vaidotas Zemlys-Balevičius -- 5. Encouraging private corporate investment in India / Hrishikesh Vinod, Honey Karun, Lekha S. Chakraborty -- 6. High-mixed frequency forecasting methods in R -- With applications to Philippine GDP and inflation / Roberto S. Mariano, Suleyman Ozmucur -- 7. Nonlinear time series in R: threshold cointegration with tsDyn / Matthieu Stigler -- Part III. Micro Econometrics -- 8. Econometric analysis of productivity: theory and implementation in R / Robin C. Sickles, Wonho Song, Valentin Zelenyuk -- 9. Stochastic frontier models using R / Giancarlo Ferrara.
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Book(print)#61996
Handbook of statistics, Vol. 14, Statistical methods in finance
In: Handbook of statistics Vol. 14
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Book(print)#82016
Handbook of statistics, volume 34, Data gathering, analysis and protection of privacy through randomized response techniques: qualitative and quantitative human traits
In: Handbook of statistics volume 34
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Statistical learning using neural networks: a guide for statisticians and data scientists
In: A Chapman & Hall book
In: Artificial intelligence
"This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples. Statistical topics covered include multivariate statistics (Cluster, Classification, Dimension Reduction, Projection Pursuit, Nonlinear Regression) Survival Analysis (Cox Model and Extensions) Control, Chart and Statistical Inference. Illustrative examples will be mainly from medicine, engineering, and economics"--
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