Abstract
This chapter deals with estimation of joint density and conditional expectation when observations form a stationary time series. We describe kernel density estimation under the assumption that the time series satisfies the strong mixing condition. We give examples wherein kernel density estimation has been applied to real life data.
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Rajarshi, M.B. (2013). Estimation of Joint Densities and Conditional Expectation. In: Statistical Inference for Discrete Time Stochastic Processes. SpringerBriefs in Statistics. Springer, India. https://doi.org/10.1007/978-81-322-0763-4_5
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DOI: https://doi.org/10.1007/978-81-322-0763-4_5
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