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R Programming for Actuarial Science
Professional resource providing an introduction to R coding for actuarial and financial mathematics applications, with real-life examples
R Programming for Actuarial Science provides a grounding in R programming applied to the mathematical and statistical methods that are of relevance for actuarial work.
In R Programming for Actuarial Science, readers will find:
• Basic theory for each chapter to complement other actuarial textbooks which provide foundational theory in depth.
• Topics covered include compound interest, statistical inference, asset-liability matching, time series, loss distributions, contingencies, mortality models, and option pricing plus many more typically covered in university courses.
• More than 400 coding examples and exercises, most with solutions, to enable students to gain a better understanding of underlying mathematical and statistical principles.
• An overall basic to intermediate level of coverage in respect of numerous actuarial applications, and real-life examples included with every topic.
Providing a highly useful combination of practical discussion and basic theory, R Programming for Actuarial Science is an essential reference for BSc/MSc students in actuarial science, trainee actuaries studying privately, and qualified actuaries with little programming experience, along with undergraduate students studying finance, business, and economics.
Professional resource providing an introduction to R coding for actuarial and financial mathematics applications, with real-life examples
R Programming for Actuarial Science provides a grounding in R programming applied to the mathematical and statistical methods that are of relevance for actuarial work.
In R Programming for Actuarial Science, readers will find:
• Basic theory for each chapter to complement other actuarial textbooks which provide foundational theory in depth.
• Topics covered include compound interest, statistical inference, asset-liability matching, time series, loss distributions, contingencies, mortality models, and option pricing plus many more typically covered in university courses.
• More than 400 coding examples and exercises, most with solutions, to enable students to gain a better understanding of underlying mathematical and statistical principles.
• An overall basic to intermediate level of coverage in respect of numerous actuarial applications, and real-life examples included with every topic.
Providing a highly useful combination of practical discussion and basic theory, R Programming for Actuarial Science is an essential reference for BSc/MSc students in actuarial science, trainee actuaries studying privately, and qualified actuaries with little programming experience, along with undergraduate students studying finance, business, and economics.