標題: Titlebook: Statistical Foundations of Actuarial Learning and its Applications; Mario V. Wüthrich,Michael Merz Book‘‘‘‘‘‘‘‘ 2023 The Authors 2023 Open [打印本頁] 作者: 夸大 時間: 2025-3-21 16:53
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作者: saturated-fat 時間: 2025-3-21 22:18 作者: 不可磨滅 時間: 2025-3-22 03:50 作者: 拋媚眼 時間: 2025-3-22 07:26 作者: N斯巴達人 時間: 2025-3-22 10:23
Mario V. Wüthrich,Michael Merzng process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event?– a作者: 鎮(zhèn)壓 時間: 2025-3-22 13:14
Mario V. Wüthrich,Michael Merzng process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event?– a作者: 撤退 時間: 2025-3-22 17:40
Mario V. Wüthrich,Michael Merzng process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event?– a作者: 我沒有命令 時間: 2025-3-23 00:07
Mario V. Wüthrich,Michael Merzng process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event?– a作者: 安慰 時間: 2025-3-23 01:40 作者: Pulmonary-Veins 時間: 2025-3-23 09:27 作者: Foreknowledge 時間: 2025-3-23 10:58
Mario V. Wüthrich,Michael Merz. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event?– a parameter作者: 施魔法 時間: 2025-3-23 17:27
Mario V. Wüthrich,Michael Merz. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event?– a parameter作者: 消散 時間: 2025-3-23 19:40
Mario V. Wüthrich,Michael Merz. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event?– a parameter作者: Hiatus 時間: 2025-3-24 00:02 作者: Esophagitis 時間: 2025-3-24 05:34
we shall give some idea of the methods of the minimum. Other types of generalized solutions, the study of which requires different procedures, will be considered instead in Chapter V (§§ 37, 38, 39).作者: 真 時間: 2025-3-24 10:16 作者: 技術 時間: 2025-3-24 14:11
Mario V. Wüthrich,Michael Merzogical inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path978-1-4419-1825-3978-0-387-21786-4Series ISSN 0172-7397 Series E-ISSN 2197-568X 作者: 隱士 時間: 2025-3-24 16:41
Mario V. Wüthrich,Michael Merzogical inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path978-1-4419-1825-3978-0-387-21786-4Series ISSN 0172-7397 Series E-ISSN 2197-568X 作者: 西瓜 時間: 2025-3-24 21:30 作者: 高度贊揚 時間: 2025-3-24 23:41 作者: ornithology 時間: 2025-3-25 06:20
Mario V. Wüthrich,Michael Merzate and postgraduate students as well as to researchers in the field of partial differential equations and nonlinear analysis..978-3-031-21855-2978-3-031-21856-9Series ISSN 2191-8198 Series E-ISSN 2191-8201 作者: outset 時間: 2025-3-25 07:45
Mario V. Wüthrich,Michael Merzrative language) that are in direct style. The residual programs are in A-normal form. A simple corollary yields CPS (continuation-passing style) terms instead. We illustrate both transformations with two interpreters for Paulson‘s Tiny language, a classical example in partial evaluation.作者: invert 時間: 2025-3-25 15:31 作者: 擴音器 時間: 2025-3-25 16:01 作者: Buttress 時間: 2025-3-25 21:02
Mario V. Wüthrich,Michael Merzg outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path作者: 租約 時間: 2025-3-26 00:23 作者: 水獺 時間: 2025-3-26 05:33 作者: 不妥協 時間: 2025-3-26 09:58 作者: 托人看管 時間: 2025-3-26 15:46 作者: concert 時間: 2025-3-26 19:06
Mario V. Wüthrich,Michael Merzg outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path作者: ALLAY 時間: 2025-3-26 21:54
Statistical Foundations of Actuarial Learning and its Applications作者: aerial 時間: 2025-3-27 01:31 作者: 過于平凡 時間: 2025-3-27 06:18 作者: AWRY 時間: 2025-3-27 10:00
2523-3262 achine learning methods.Discusses the state-of-the-art in prThis open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents th作者: THROB 時間: 2025-3-27 16:59
Exponential Dispersion Family,important classes of distribution functions for regression modeling. They include, among others, the Gaussian, the binomial, the Poisson, the gamma, the inverse Gaussian distributions, as well as Tweedie’s models. We introduce these families of distribution functions, discuss their properties and pr作者: echnic 時間: 2025-3-27 17:57 作者: SPURN 時間: 2025-3-27 23:23 作者: 碎片 時間: 2025-3-28 05:55 作者: LOPE 時間: 2025-3-28 06:20
Bayesian Methods, Regularization and Expectation-Maximization,arlo (MCMC) method for model fitting. We discuss regularization of regression models such as ridge and LASSO regularization, which has a Bayesian interpretation, and we consider the Expectation-Maximization (EM) algorithm. The EM algorithm is a general purpose tool that can handle incomplete data se作者: adulterant 時間: 2025-3-28 11:51 作者: 預測 時間: 2025-3-28 15:36 作者: echnic 時間: 2025-3-28 20:20
Convolutional Neural Networks,deling, e.g., applied to image recognition problems. Time-series and images have a natural topology, and CN networks try to benefit from this additional structure (over tabular data). We introduce these network architectures and provide insurance-relevant examples related to telematics data and mort作者: CHASE 時間: 2025-3-29 02:01
Natural Language Processing, words can be embedded into low-dimension spaces that serve as numerical word encodings. These can then be used for text recognition, either using RN networks or attention layers. We give an example where we aim at predicting claim perils from claim descriptions.作者: hermitage 時間: 2025-3-29 05:30 作者: 滲入 時間: 2025-3-29 08:57 作者: Osteons 時間: 2025-3-29 14:48
Predictive Modeling and Forecast Evaluation,odels. This chapter is complemented by a more decision-theoretic approach to forecast evaluation, it discusses deviance losses, proper scoring, elicitability, forecast dominance, cross-validation, Akaike’s information criterion (AIC) and we give an introduction to the bootstrap simulation method.作者: 卡死偷電 時間: 2025-3-29 17:10
Deep Learning,e model uncertainty. This chapter is complemented by many examples on non-life insurance pricing, but also on mortality modeling, as well as tools that help to explain deep FN network regression results.作者: Integrate 時間: 2025-3-29 21:27 作者: 鐵砧 時間: 2025-3-30 03:52 作者: 拉開這車床 時間: 2025-3-30 07:52
Statistical Foundations of Actuarial Learning and its Applications978-3-031-12409-9Series ISSN 2523-3262 Series E-ISSN 2523-3270
