ERN: Statistical Decision Theory; Operations Research (Topic)最新文献

{"title":"Early Reservation for Follow-up Appointments in a Slotted-Service Queue","authors":"Yichuan Ding, D. Gupta, Xiaoxu Tang","doi":"10.2139/ssrn.3616565","DOIUrl":"https://doi.org/10.2139/ssrn.3616565","url":null,"abstract":"Shall Follow-up Appointments Be Booked in Advance? Appointment systems are ubiquitous, especially in healthcare. By looking into a large data set with over 1.6 million appointments, we observe that many doctors booked a follow-up appointment at the end of their meeting with their patients. This strategy ensures that the patients would follow up but at the risk that the patient may not show up and the appointment ends being wasted. We develop a slotted-service queue model to study if and when such a strategy should be used in three representative appointment systems, respectively. In an open access system, it is optimal to never use this strategy. In a traditional appointment system that allows patients to book in advance, it is optimal to apply this strategy to some patients. While in a hybrid system with both walk-in patients and patients with appointments, whether to use this strategy depends on the load balancing between the two patient queues.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121381478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exercises in Stochastic Analysis","authors":"V. Lucic","doi":"10.2139/ssrn.3590970","DOIUrl":"https://doi.org/10.2139/ssrn.3590970","url":null,"abstract":"This, somewhat unusual collection of problems in Measure-Theoretic Probability and Stochastic Analysis, should have been more appropriately called: ``The Problems I Like\". For the material borrowed from the existing literature, full references and occasional historical remarks are provided. There are also original problems: Problem 3, Problem 22, Problem 25 (jointly worked out with Vlado Kesselj), Problem 27 (jointly worked out with Yacine Dolivet), Problem 39, Problem 49, Problem 50, Problem 51, Problem 56 Problem 58 (due to Alex Duriez), Problem 59, and Problem 65 are original. As this collection is work in progress, and any comments from the readers will be greatly appreciated.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134421712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Note on First Passage Time Densities for Stochastic Simulation of Diffusion","authors":"R. Lichters","doi":"10.2139/ssrn.3577396","DOIUrl":"https://doi.org/10.2139/ssrn.3577396","url":null,"abstract":"This brief note extends our working paper [1]. A central claim in [1] is that the first passage time densities in 1-3 dimensions have upper bounds that allow the implementation of efficient von Neumann rejection schemes for generating random first passage times. This approach was adopted, among other methods, in [4]. The claim mentioned above was not proved in [1]. This note attempts to close this gap using the arguments provided in [2].","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115167418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural Estimation of Intertemporal Externalities on ICU Admission Decisions","authors":"Yiwen Shen, Carri W. Chan, Fanyin Zheng, G. Escobar","doi":"10.2139/ssrn.3564776","DOIUrl":"https://doi.org/10.2139/ssrn.3564776","url":null,"abstract":"Service systems’ behavior can be affected by multiple factors. In the case of intensive care units (ICUs), which admit patients from four primary loci (the emergency department (ED), scheduled patients, planned transfers from other ICUs, and unplanned transfers), it is known that admission rates of some patients decrease as occupancy increases. It is also known that, for at least some conditions, ICU admission is not just a function of patients’ illness, and that a significant proportion of the variation in ICU admission rates is due to hospital, not patient, factors. In this paper, we employ two years of data from patients admitted to 21 Kaiser Permanente Northern California ICUs from the ED. We quantify the variation in ICU admission from the ED under varying degrees of ICU and ED occupancy. We find that substantial heterogeneity in admission rates is present, and that it cannot be explained either by patient factors or occupancy levels alone. We use a structural model to understand the extent that inter-temporal externalities could account for some of this variation. Using counterfactual simulations, we find that, if hospitals had more information regarding their behaviors, and if it were possible to alter hospital admission processes to incorporate such information, hospitals could achieve greater efficiency safely.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121588141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining a Smart Pricing Policy with a Simple Replenishment Policy: Managing Uncertainties in the Presence of Stochastic Purchase Returns","authors":"Alys Jiaxin Liang, Stefanus Jasin, J. Uichanco","doi":"10.2139/ssrn.3563847","DOIUrl":"https://doi.org/10.2139/ssrn.3563847","url":null,"abstract":"Regarded as the ``ticking time bomb'' in the industry, returns have cost retailers hundreds of billions of dollars in the US. This has prompted businesses to adapt by charging extra delivery fees, increasing prices (to compensate for the return cost) or allowing ``returnless refunds''. Undesirable as returns are, it is generally accepted that they cannot be entirely eliminated and lenient return policies are necessary to maintain customer loyalty. Motivated by this reality, we ask: How can a retailer offering a free return and refund policy improve profitability through joint inventory and pricing control? We model a single store/warehouse setting with lost sales, positive lead time, periodic review, and Binomial (Poisson) demand. Any purchase can be returned at a full refund within a grace period and might be restocked after passing inspection. Jointly optimizing inventory and pricing is challenging since the state variables must track the return status of all past purchases. We develop an easy-to-implement heuristic policy that combines a ``smart'' adaptive pricing policy with a very simple replenishment policy. Our key insight is that uncertainties in both demands and returns are effectively managed by the price control. We show that the relative loss of this policy converges to zero at a rate much faster than the usual square-root when the annual market size becomes large. In addition, our results can be extended to more general settings including: (1) return fees and partial refunds; (2) non-stationary demand rate functions; and, (3) service level constraints.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115749724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Demand With Unobserved No-Purchases on Revenue-Managed Data","authors":"Anran Li, K. Talluri","doi":"10.2139/ssrn.3525773","DOIUrl":"https://doi.org/10.2139/ssrn.3525773","url":null,"abstract":"Discrete-choice models such as the multinomial-logit (MNL) model are increasingly being used to model customer purchase behaviour in hotels, airlines, fashion retail and e-commerce. Their estimation however has proved difficult in practice. One reason for this difficulty is well-known: most firms in retail do not, or unable to, record customers who were interested in purchasing but did not buy the product (``no-purchasers\"). Estimating demand even with the simplest discrete-choice model such as the MNL becomes challenging then as we do not know the fraction that have chosen an outside option (not purchased). Indeed, the parameters of the MNL model may not be identifiable with such data. Some previous approaches have proposed using ``market-share\" to pin down the parameter associated with the outside option. However, in many industries, market-share data is difficult to obtain, and for some, such as fashion products, has little meaning. In this paper we point out an additional difficulty that arises in practice: Many firms constantly monitor sales and optimize their prices and assortments based on partially observed demand. This leads to an optimization-induced endogeneity as the input used for estimation has been processed by optimization that takes both past data as well as future demand trends in setting controls. As we demonstrate, methods that work well on randomly generated assortments may do badly on optimized assortment data. In this paper we propose a robust method when the firm cannot observe no-purchases, has no market-share information, and the optimization-induced endogeneity exists in the data. The method is a two-step GMM (Generalized Method of Moments) procedure for which we show the estimates are consistent, and we give intuition for its robustness. In Monte-Carlo simulations the performance of our method matches existing methods on randomly generated controls, and is superior in accuracy and robustness when optimization-induced endogeneity is present. On a large real-world data set from the fashion industry --- subject to markdowns as well as stock-outs and unknown management controls --- our method provides very reasonable and robust estimates compared to existing methods.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127966413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fortnite: The Business Model Pattern Behind the Scene","authors":"Timo Schöber, G. Stadtmann","doi":"10.2139/ssrn.3520155","DOIUrl":"https://doi.org/10.2139/ssrn.3520155","url":null,"abstract":"We analyse the business model pattern behind the success of the Fortnite game. A theoretical model is used to examine the conditions where a Freemium strategy is appropriate. We also shed light on the structure of the in-game-shop and analyse several features from a marketing perspective.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122595225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Strategies for Advance Selling With Random Rewards","authors":"Liangjun Peng, Mengdi Gu, Yuge Bai","doi":"10.2139/ssrn.3508481","DOIUrl":"https://doi.org/10.2139/ssrn.3508481","url":null,"abstract":"Advance selling helps retailers who often face a newsvendor problem to reduce demand uncertainty. With the development of e-commerce and promotion, retailers are no longer limited to a single advance selling strategy. Random rewards promotion becomes increasingly easy to apply with the support of network and information technology. Advance selling strategy considering random rewards is favoured by retailers because it not only caters to the psychological needs of strategic consumers but also helps retailers reduce the uncertainty of demand. The advance selling strategy considering random rewards is an optimization model that is established on the basis of single advance selling strategy model. The objective function of the model is retailer’s total profit. The random rewards mechanism is modelled according to the actual situation. The expected utility of consumers’ random rewards adopts a power function of risk aversion type based on strategic consumer reactions. This study analytically examines the optimal total profit of a retailer under the two advance selling strategy models and finds that there is threshold that determines whether an advance selling strategy considers random rewards. In addition, we compare and analyse the advance selling price and consumer utility under the two strategies. Results comparison reveals that the difference between the amount of discount and the size of the winning prize’s expected utility under single advance selling strategy determines whether the advance selling price adopts a discount sales strategy. Numerical analysis further validates the results of the study.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"9 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128296846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Conditional Eigenvalue GARCH","authors":"S. Hetland, R. Pedersen, Anders Rahbek","doi":"10.2139/ssrn.3505302","DOIUrl":"https://doi.org/10.2139/ssrn.3505302","url":null,"abstract":"In this paper we consider a multivariate generalized autoregressive conditional heteroskedastic (GARCH) class of models where the eigenvalues of the conditional covariance matrix are time-varying. The proposed dynamics of the eigenvalues is based on applying the general theory of dynamic conditional score models as proposed by Creal, Koopman and Lucas (2013) and Harvey (2013). We denote the obtained GARCH model with dynamic conditional eigenvalues (and constant conditional eigenvectors) as the ?-GARCH model. We provide new results on asymptotic theory for the Gaussian QMLE, and for testing of reduced rank of the (G)ARCH loading matrices of the time-varying eigenvalues. The theory is applied to US data, where we ?find that the eigenvalue structure can be reduced similar to testing for the number in factors in volatility models.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123609481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald","authors":"C. Manski","doi":"10.3386/w26596","DOIUrl":"https://doi.org/10.3386/w26596","url":null,"abstract":"Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes use of statistical decision theory to evaluate the performance of models in decision making. I consider the common practice of \u0000 as‐if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as‐if optimization or any other model‐based decision rule by its performance across the state space, listing all states of nature that one believes feasible, not across the model space. I apply the theme to prediction and treatment choice. Statistical decision theory is conceptually simple, but application is often challenging. Advancing computation is the primary task to complete the foundations sketched by Haavelmo and Wald.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133939804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}