標(biāo)題: Titlebook: Effective Statistical Learning Methods for Actuaries I; GLMs and Extensions Michel Denuit,Donatien Hainaut,Julien Trufin Textbook 2019 Spri [打印本頁] 作者: 鳥場 時(shí)間: 2025-3-21 20:08
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書目名稱Effective Statistical Learning Methods for Actuaries I影響因子(影響力)學(xué)科排名
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書目名稱Effective Statistical Learning Methods for Actuaries I網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Effective Statistical Learning Methods for Actuaries I被引頻次
書目名稱Effective Statistical Learning Methods for Actuaries I被引頻次學(xué)科排名
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書目名稱Effective Statistical Learning Methods for Actuaries I年度引用學(xué)科排名
書目名稱Effective Statistical Learning Methods for Actuaries I讀者反饋
書目名稱Effective Statistical Learning Methods for Actuaries I讀者反饋學(xué)科排名
作者: 暫時(shí)休息 時(shí)間: 2025-3-21 21:14
Exponential Dispersion (ED) Distributionsques. The objective functions used to calibrate the regression models described in this book correspond to log-likelihoods taken from this family. This is why a good knowledge of these models is the necessary prerequisite to the next chapters, in order to understand which objective function to use a作者: 憤怒事實(shí) 時(shí)間: 2025-3-22 01:31
Maximum Likelihood Estimationors enjoy convenient theoretical properties, being optimal in a wide variety of situations. The maximum likelihood principle will be used throughout the next chapters to fit the supervised learning models.作者: molest 時(shí)間: 2025-3-22 04:49 作者: 小卒 時(shí)間: 2025-3-22 12:37
Over-Dispersion, Credibility Adjustments, Mixed Models, and Regularizationy results in correlation among the responses within the same group, casting doubts about the outputs of analyses assuming mutual independence. Random effects offer a convenient way to model such grouping structure. This chapter presents the Generalized Linear Mixed Model (GLMM) approach to regressio作者: 易改變 時(shí)間: 2025-3-22 13:43
Generalized Additive Models (GAMs)eatures coded by means of binary variables. However, this assumption becomes questionable for continuous features which may have a nonlinear effect on the score scale. This chapter is devoted to Generalized Additive Models (GAMs) which keep the additive decomposition of the score but allow the actua作者: 易改變 時(shí)間: 2025-3-22 17:59
Beyond Mean Modeling: Double GLMs and GAMs for Location, Scale and Shape (GAMLSS)ion, scale, shape or probability mass at the origin, for instance. This allows the actuary to let the available information enter other dimensions of the response, such as volatility or no-claim probability. The double GLM setting supplements GLMs with dispersion modeling, letting the dispersion par作者: 有花 時(shí)間: 2025-3-22 22:29
Some Generalized Non-linear Models (GNMs) to be learned from the data. GAMs can be fitted with the help of local versions of GLMs or by decomposing the nonlinear effects of the features in an appropriate spline basis so that the working scores are also linear functions of the regression parameters. In this chapter, models with a score invo作者: Ballad 時(shí)間: 2025-3-23 03:39
Extreme Value Modelstions, with a particular emphasis on large claims in property and casualty insurance and mortality at oldest ages in life insurance. Large claims generally affect liability coverages and require a separate analysis. The reason for a separate analysis of small or moderate losses (also referred to as 作者: 排他 時(shí)間: 2025-3-23 06:54
Over-Dispersion, Credibility Adjustments, Mixed Models, and Regularizationn analysis. In this framework, random effects are added on the same scale as the linear combination of the available features (called fixed effects). Predictive distributions, that is, conditional distribution of the response given past experience, are particularly attractive to re-valuate future premiums based on claims observed previously.作者: Ergots 時(shí)間: 2025-3-23 10:52 作者: heart-murmur 時(shí)間: 2025-3-23 14:46 作者: 利用 時(shí)間: 2025-3-23 20:14
Exponential Dispersion (ED) Distributionss is why a good knowledge of these models is the necessary prerequisite to the next chapters, in order to understand which objective function to use according to the format of the response. Particular attention is paid to the effect of averaging and weighting with ED distributions.作者: NOCT 時(shí)間: 2025-3-24 01:48 作者: chassis 時(shí)間: 2025-3-24 03:46 作者: Gnrh670 時(shí)間: 2025-3-24 08:20 作者: investigate 時(shí)間: 2025-3-24 13:12 作者: alcohol-abuse 時(shí)間: 2025-3-24 16:08
Generalized Additive Models (GAMs)rmation of continuous features for inclusion on the score scale. Precisely, continuous features enter the model in a semi-parametric additive predictor. Typical applications in insurance include the graduation of mortality and morbidity or the analysis of risk variation by age or geographic area, for instance.作者: accrete 時(shí)間: 2025-3-24 22:17 作者: addict 時(shí)間: 2025-3-24 23:58 作者: 清晰 時(shí)間: 2025-3-25 05:28 作者: cluster 時(shí)間: 2025-3-25 10:46 作者: 重力 時(shí)間: 2025-3-25 12:28 作者: EXUDE 時(shí)間: 2025-3-25 19:33
Some Generalized Non-linear Models (GNMs) with the GLM machinery. The prominent example consists in products of time-specific and age-specific factors in mortality projections. This chapter is devoted to some special cases of GNMs that are particularly useful for insurance applications.作者: 表皮 時(shí)間: 2025-3-25 23:48 作者: 是剝皮 時(shí)間: 2025-3-26 03:02 作者: follicle 時(shí)間: 2025-3-26 07:15 作者: ARENA 時(shí)間: 2025-3-26 11:07
https://doi.org/10.1007/978-3-662-39757-2ges at death above 95 for extinct cohorts born in Belgium between 1886 and 1904. The analysis supports the existence of an upper limit to human lifetime for these cohorts. Therefore, assuming that the force of mortality becomes ultimately constant, that is, that the remaining lifetime distribution t作者: 凝結(jié)劑 時(shí)間: 2025-3-26 16:36 作者: 草率女 時(shí)間: 2025-3-26 19:44 作者: Mediocre 時(shí)間: 2025-3-26 23:23 作者: Proclaim 時(shí)間: 2025-3-27 01:14 作者: PRO 時(shí)間: 2025-3-27 08:36
,Das Reichselektrizit?tsmonopol,y results in correlation among the responses within the same group, casting doubts about the outputs of analyses assuming mutual independence. Random effects offer a convenient way to model such grouping structure. This chapter presents the Generalized Linear Mixed Model (GLMM) approach to regressio作者: Crohns-disease 時(shí)間: 2025-3-27 09:50
,Die Landes-Versicherungs?mter,eatures coded by means of binary variables. However, this assumption becomes questionable for continuous features which may have a nonlinear effect on the score scale. This chapter is devoted to Generalized Additive Models (GAMs) which keep the additive decomposition of the score but allow the actua作者: 售穴 時(shí)間: 2025-3-27 16:42
https://doi.org/10.1007/978-3-642-94474-1ion, scale, shape or probability mass at the origin, for instance. This allows the actuary to let the available information enter other dimensions of the response, such as volatility or no-claim probability. The double GLM setting supplements GLMs with dispersion modeling, letting the dispersion par作者: 出處 時(shí)間: 2025-3-27 20:47 作者: dysphagia 時(shí)間: 2025-3-27 22:13 作者: 寬大 時(shí)間: 2025-3-28 04:46 作者: 拖債 時(shí)間: 2025-3-28 06:18
Springer Actuarialhttp://image.papertrans.cn/e/image/302810.jpg作者: 鋼盔 時(shí)間: 2025-3-28 12:48
Effective Statistical Learning Methods for Actuaries I978-3-030-25820-7Series ISSN 2523-3262 Series E-ISSN 2523-3270 作者: 現(xiàn)暈光 時(shí)間: 2025-3-28 16:42
https://doi.org/10.1007/978-3-642-93176-5 carefully explained, referring to the appropriate literature for related problems not covered here. The nature of data available to perform insurance studies is also discussed, stressing the inherent limitations in the interpretation of conclusions drawn from observational studies.作者: ARCHE 時(shí)間: 2025-3-28 21:48 作者: 對手 時(shí)間: 2025-3-29 01:48
Insurance Risk Classification carefully explained, referring to the appropriate literature for related problems not covered here. The nature of data available to perform insurance studies is also discussed, stressing the inherent limitations in the interpretation of conclusions drawn from observational studies.作者: 擦掉 時(shí)間: 2025-3-29 05:12
Maximum Likelihood Estimationors enjoy convenient theoretical properties, being optimal in a wide variety of situations. The maximum likelihood principle will be used throughout the next chapters to fit the supervised learning models.作者: DAUNT 時(shí)間: 2025-3-29 10:24 作者: 反復(fù)拉緊 時(shí)間: 2025-3-29 12:07
