{"title":"Avishek Nag","description":"\u003cp\u003eDelve into the captivating worlds crafted by Avishek Nag, where thrilling adventures and intricate characters await. Perfect for fans of epic fantasy and immersive storytelling. Start your journey here.\u003c\/p\u003e","products":[{"product_id":"survival-analysis-with-python-book-avishek-nag-9781032073675","title":"Survival Analysis with Python","description":"Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into          Parametric models with coverage of        Concept of maximum likelihood estimate (MLE) of a probability distribution parameter      MLE of the survival function      Common probability distributions and their analysis      Analysis of exponential distribution as a survival function      Analysis of Weibull distribution as a survival function      Derivation of Gumbel distribution as a survival function from Weibull         Non-parametric models including        Kaplan–Meier (KM) estimator, a derivation of expression using MLE      Fitting KM estimator with an example dataset, Python code and plotting curves      Greenwood’s formula and its derivation         Models with covariates explaining        The concept of time shift and the accelerated failure time (AFT) model      Weibull-AFT model and derivation of parameters by MLE      Proportional Hazard (PH) model      Cox-PH model and Breslow’s method      Significance of covariates      Selection of covariates     The Python lifelines library is used for coding examples. 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It then delves into          Parametric models with coverage of        Concept of maximum likelihood estimate (MLE) of a probability distribution parameter      MLE of the survival function      Common probability distributions and their analysis      Analysis of exponential distribution as a survival function      Analysis of Weibull distribution as a survival function      Derivation of Gumbel distribution as a survival function from Weibull         Non-parametric models including        Kaplan–Meier (KM) estimator, a derivation of expression using MLE      Fitting KM estimator with an example dataset, Python code and plotting curves      Greenwood’s formula and its derivation         Models with covariates explaining        The concept of time shift and the accelerated failure time (AFT) model      Weibull-AFT model and derivation of parameters by MLE      Proportional Hazard (PH) model      Cox-PH model and Breslow’s method      Significance of covariates      Selection of covariates     The Python lifelines library is used for coding examples. 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This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python.    The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. You’ll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You’ll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE).     Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.    What You Will Learn      Understand applied probability and statistics with finance Design forecasting models of the stock price with the stochastic process, Monte-Carlo simulation. Option price estimation with both risk-neutral probabilistic and PDE-driven approach. 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