This book, using R and RStudio, demonstrates how to render an audit opinion that is legally and statistically defensible; analyze, extract, and manipulate accounting data; build a risk assessment matrix to inform the conduct of a cost-effective audit program; and more.
Adaptive filtering still receives attention in engineering as the use of the adaptive filter provides improved performance over the use of a fixed filter under the time-varying and unknown statistics environments.
This book presents fundamentals in MATLAB programming, including data and statement structures, control structures, function writing and bugging in MATLAB programming, followed by the presentations of algebraic computation, transcendental function evaluations and data processing.
R for Health Technology Assessment discusses the use of proper statistical software, specifically R, to perform the whole pipeline of analytic modelling in health technology assessment (HTA).
Written by an experienced operations research practitioner with a strong applied mathematics background, this book offers practical insights into how to approach optimization problems, how to develop intelligent and efficient mathematical models and algorithms, and how to implement and deliver software products to customers.
Written by an experienced operations research practitioner with a strong applied mathematics background, this book offers practical insights into how to approach optimization problems, how to develop intelligent and efficient mathematical models and algorithms, and how to implement and deliver software products to customers.
Now in its second edition, this edited book presents recent progress and techniques in partial least squares path modeling (PLS-PM), and provides a comprehensive overview of the current state-of-the-art in PLS-PM research.
This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata.
This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata.
Dieses Buch könnte interessant für Sie sein, falls Sie über eine solide mathematische Ausbildung verfügen und nun Anwendungsprobleme mit Hilfe von Optimierungsmodellen lösen möchten, ohne sich zuvor jahrelang mit der zugehörigen Theorie zu beschäftigen.
This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications.
Dieses Buch könnte interessant für Sie sein, falls Sie über eine solide mathematische Ausbildung verfügen und nun Anwendungsprobleme mit Hilfe von Optimierungsmodellen lösen möchten, ohne sich zuvor jahrelang mit der zugehörigen Theorie zu beschäftigen.
This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications.
This book covers all the topics found in introductory descriptive statistics courses, including simple linear regression and time series analysis, the fundamentals of inferential statistics (probability theory, random sampling and estimation theory), and inferential statistics itself (confidence intervals, testing).
Future microwave, wireless communication systems, computer chip designs, and sensor systems will require miniature fabrication processes in the order of nanometers or less as well as the fusion of various material technologies to produce composites consisting of many different materials.
Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called "e;ADAM"e; (Augmented Dynamic Adaptive Model).
Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called "e;ADAM"e; (Augmented Dynamic Adaptive Model).
This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata.
This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata.
Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data.
This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data.
