Titlebook: Mathematical Statistics; Jun Shao Textbook 2003Latest edition Springer-Verlag New York 2003 Mathematical Statistics.Statistical Theory.lik

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Unbiased Estimation,.2. In this chapter, we discuss in detail how to derive unbiased estimators and, more importantly, how to find the best unbiased estimators in various situations. Although an unbiased estimator (even the best unbiased estimator if it exists) is not necessarily better than a slightly biased estimator

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Hypothesis Tests, observed ., we test a given hypothesis . : . ∈ . versus . : . ∈ ., where . and . are two disjoint subsets of . and .∪. = .. Notational conventions and basic concepts (such as two types of errors, significance levels, and sizes) given in Example 2.20 and §2.4.2 are used in this chapter.

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https://doi.org/10.1007/b97553Mathematical Statistics; Statistical Theory; likelihood; Markov chain; Mathematica; mathematical statisti

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Springer Texts in Statisticshttp://image.papertrans.cn/m/image/626590.jpg

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Fundamentals of Statistics,This chapter discusses some fundamental concepts of mathematical statistics. These concepts are essential for the material in later chapters.

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Estimation in Parametric Models,In this chapter, we consider point estimation methods in parametric models. One such method, the moment method, has been introduced in §3.5.2. It is assumed in this chapter that the sample . is from a population in a parametric family .={. : . ∈ Θ}, where Θ ⊂ . for a fixed integer . ≥ 1.