Relevant bibliographies by topics / Optimal sample allocation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Optimal sample allocation'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Optimal sample allocation"

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Royset, Johannes O., and Roberto Szechtman. "Optimal Budget Allocation for Sample Average Approximation." Operations Research 61, no. 3 (2013): 762–76. http://dx.doi.org/10.1287/opre.2013.1163.

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Wright, Tommy. "Exact optimal sample allocation: More efficient than Neyman." Statistics & Probability Letters 129 (October 2017): 50–57. http://dx.doi.org/10.1016/j.spl.2017.04.026.

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Kaputa, Stephen J., and Katherine J. Thompson. "Adaptive Design Strategies for Nonresponse Follow-Up in Economic Surveys." Journal of Official Statistics 34, no. 2 (2018): 445–62. http://dx.doi.org/10.2478/jos-2018-0020.

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Abstract The U.S. Census Bureau is investigating nonrespondent subsampling strategies for use in the 2017 Economic Census. In previous research, we developed an optimized allocation procedure for subsampling nonrespondents that selects larger systematic samples in domains with lower initial response. This article expands on our previous research by exploring improvements to the optimal allocation method; we investigate refinements to the previous procedure that incorporate measures of respondent balance with respect to the original sample. The revised allocation procedures have simultaneous ob
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Conti, Stefano, and Karl Claxton. "Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design." Medical Decision Making 29, no. 6 (2009): 643–60. http://dx.doi.org/10.1177/0272989x09336142.

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Bayesian decision theory can be used not only to establish the optimal sample size and its allocation in a single clinical study but also to identify an optimal portfolio of research combining different types of study design. Within a single study, the highest societal payoff to proposed research is achieved when its sample sizes and allocation between available treatment options are chosen to maximize the expected net benefit of sampling (ENBS). Where a number of different types of study informing different parameters in the decision problem could be conducted, the simultaneous estimation of
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Zhang, Joanne. "Optimal Sample Size Allocation in a Thorough QTc Study." Drug Information Journal 45, no. 4 (2011): 455–68. http://dx.doi.org/10.1177/009286151104500407.

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Lai, Dejian, Kuang-Chao Chang, Mohammad H. Rahbar, and Lemuel A. Moye. "Optimal Allocation of Sample Sizes to Multicenter Clinical Trials." Journal of Biopharmaceutical Statistics 23, no. 4 (2013): 818–28. http://dx.doi.org/10.1080/10543406.2013.789884.

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Friedrich, Ulf, Ralf Münnich, and Martin Rupp. "Multivariate optimal allocation with box-constraints." Austrian Journal of Statistics 47, no. 2 (2018): 33–52. http://dx.doi.org/10.17713/ajs.v47i2.764.

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Modern surveys aim at fostering accurate information on demographic and other variables. The necessity for providing figures on regional levels and on a variety of subclasses leads to fine stratifications of the population. Optimizing the accuracy of stratified random samples requires incorporating a vast amount of strata on various levels of aggregation. Accounting for several variables of interest for the optimization yields a multivariate optimal allocation problem in which practical issues such as cost restrictions or control of sampling fractions have to be considered. Taking advantage of
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Bernardelli, Michał, and Barbara Kowalczyk. "Optimal Allocation of the Sample in the Poisson Item Count Technique." Acta Universitatis Lodziensis. Folia Oeconomica 3, no. 335 (2018): 35–47. http://dx.doi.org/10.18778/0208-6018.335.03.

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Indirect methods of questioning are of utmost importance when dealing with sensitive questions. This paper refers to the new indirect method introduced by Tian et al. (2014) and examines the optimal allocation of the sample to control and treatment groups. If determining the optimal allocation is based on the variance formula for the method of moments (difference in means) estimator of the sensitive proportion, the solution is quite straightforward and was given in Tian et al. (2014). However, maximum likelihood (ML) estimation is known from much better properties, therefore determining the op
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Shen, Zuchao, and Benjamin Kelcey. "Optimal Sample Allocation Under Unequal Costs in Cluster-Randomized Trials." Journal of Educational and Behavioral Statistics 45, no. 4 (2020): 446–74. http://dx.doi.org/10.3102/1076998620912418.

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Conventional optimal design frameworks consider a narrow range of sampling cost structures that thereby constrict their capacity to identify the most powerful and efficient designs. We relax several constraints of previous optimal design frameworks by allowing for variable sampling costs in cluster-randomized trials. The proposed framework introduces additional design considerations and has the potential to identify designs with more statistical power, even when some parameters are constrained due to immutable practical concerns. The results also suggest that the gains in efficiency introduced
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Chen, Weiwei, Siyang Gao, Chun-Hung Chen, and Leyuan Shi. "An Optimal Sample Allocation Strategy for Partition-Based Random Search." IEEE Transactions on Automation Science and Engineering 11, no. 1 (2014): 177–86. http://dx.doi.org/10.1109/tase.2013.2251881.

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Dissertations / Theses on the topic "Optimal sample allocation"

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Shen, Zuchao. "Optimal Sample Allocation in Multilevel Experiments." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1553528863915366.

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Lin, Wan-Chin, and 林琬津. "Optimal Sample Sizes for Behrens-Fisher Problem—with Allocation Constraints." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/99881721817606352291.

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碩士<br>中原大學<br>應用數學研究所<br>98<br>The Behrens–Fisher problem is the problem concerning the mean differences between two normally distributed populations and assuming that the variances of the two populations are unequal or unknown, based on two independent samples. In the thesis, we consider using Welch‘s t test to evaluate power to find optimal sample sizes. First, adjusting the test statistic as exact distribution and adjusting the critical value as a function of Beta distribution. Second, discussing that what are the two optimal sample sizes required to attain the specified power level. The fi
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Morgan, Clayton David. "A sample-path optimization approach for optimal resource allocation in stochastic projects." 2006. http://www.lib.ncsu.edu/theses/available/etd-11082006-184556/unrestricted/etd.pdf.

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Tai, Chih-Ying, and 戴志穎. "Optimal Sample Size Allocation for a Series System under Accelerated Life Tests." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/527r7d.

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碩士<br>國立中央大學<br>統計研究所<br>105<br>In accelerated life tests of system reliability, the sample size allocation under different stress levels could affect the accuracy of the reliability inference. Given three stress levels of an accelerated variable, this thesis tackles the issue on the optimal allocation of an accelerated life test of series systems. It turns out that the objective functions frequently are of the form of the product of second elementary symmetric functions. We fist derive the sufficient condition when the optimal plan is reduced to a two-level test with equal sample size allocat
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Lee, I.-Chen, and 李宜真. "Optimal Sample Size Allocation for Accelerated Degradation Test (Based on Exponential Dispersion Model)." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/15800875410986355797.

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碩士<br>國立清華大學<br>統計學研究所<br>99<br>Accelerated Degradation tests (ADTs) are widely used to assess the lifetime information of highly reliable products possessing quality characteristics that both degrade over time and can be related to reliability. Hence, how to design an efficient ADT plan for assessing product’s lifetime information at normal-use stress (especially for the optimal sample-size allocation to higher test-stress levels) turns out to be a challenging issue for reliability analysts. In the literature, several papers had addressed this decision problem. However, the results are only b
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Chiu, Chun-Chih, and 邱俊智. "A Novel Optimal Sample Allocation Strategy with Meta-heuristic for Discrete Simulation Optimization." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/82en6b.

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陳玟穎. "Optimal Sample Size Allocation for Accelerated Degradation Test (based on Exponential Dispersion Model andV-optimality Criterion)." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/18501934393076313934.

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碩士<br>國立清華大學<br>統計學研究所<br>100<br>Accelerated degradation test (ADT) is widely used to assess the lifetime information (e.g.,p-thquantileor mean-time-to-failure (MTTF))of highly reliable products. Hence,it is a challenging issue for reliabilityengineer to plan an efficientADT test. Recently, Lee (2011) proposedan exponential-dispersion accelerated degradation (EDAD) model and derived the analyticalsolution of optimal sample-size allocation. The advantage of this resultis that EDAD model covers well-knownmodels such as Wiener, Gamma and Inverse Gaussian accelerated degradation model. However, th
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"Optimal sample size allocation for multi-level stress testing with extreme value regression under type-I censoring." 2012. http://library.cuhk.edu.hk/record=b5549162.

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在多組壽命試驗中，為了準確地估計模型的參數，我們必須找出最合適的實驗品數量，以分配給每一個應力水平。近來, Ng, Chan and Balakrishnan(2006)，在完整樣本情況下，利用「極值回歸模型」發展了找尋實驗品數量最合適的分配方法。其後，Ka, Chan, Ng and Balakrishnan (2011)在同一個回歸模型下，研究了對於「II型截尾樣本」最合適的分配方法。因為我們仍未確立對「I型截尾樣本」的最合適分配方法，所以我們將會在本篇論文中探討如何在「I型截尾壽命試驗」中找出最合適的實驗品分配方法。<br>在本論文中，我們會利用最大似然估計的方法去估計模型參數。我們也會計算出「逆費雪訊息矩陣」(「漸近方差協方差矩陣」)I⁻¹，用以量度參數估計值的準確度。以下是三個對最合適分配方法的決定準則:<br>1.費雪訊息矩陣的行列式最大化,<br>2. ν1估計值的方差最小化, var( ν1)(V -優化準則 )<br>3.漸近方差協方差矩陣的跡最小化, tr(⁻¹)(A-優化準則 )<br>我們也會討論在「極值回歸模型」的特例：「指數回歸模型」之下最合適的分配方法。<br>In multi-group life-testing experiment, it is essential to optimize the allocation of the items u
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Lin, Tin-Han, and 林廷翰. "Optimal Sample Size Allocation for Accelerated Life Test with Multiple Levels of Stress under Location-Scale Distributions." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/22qm9x.

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碩士<br>淡江大學<br>數學學系數學與數據科學碩士班<br>106<br>Accelerated life test is widely used to assess the lifetime information (e.g., p-th quantile or mean-time-to-failure (MTTF)) of the highly reliable products. Hence, how to design an efficient accelerated life test plan for assessing the product’s lifetime information at normal-use stress such as the optimal sample-size allocation turns out to be a challenging issue for reliability analysts. In this paper, motivated by a mylar-polyurethane data, we first proposed an accelerated life model that random error is a location-scale distribution. Next, by using t
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Teng, Zhaoyang. "Optimal and adaptive designs for multi-regional clinical trials with regional consistency requirement." Thesis, 2015. https://hdl.handle.net/2144/15706.

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To shorten the time for drug development and regulatory approval, a growing number of clinical trials are being conducted in multiple regions simultaneously. One of the challenges to multi-regional clinical trials (MRCT) is how to utilize the data obtained from other regions within the entire trial to help make local approval decisions. In addition to the global efficacy, the evidence of consistency in treatment effects between the local region and the entire trial is usually required for regional approval. In recent years, a number of statistical models and consistency criteria have been prop
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Books on the topic "Optimal sample allocation"

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Back, Kerry E. Equilibrium and Efficiency. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0004.

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Pareto optima and competitive equilibria are defined. Allocations are functions of market wealth (sharing rules) in Pareto optima, which means that all risks except market wealth are perfectly shared. Equilibria in complete markets are shown to be equivalent to Arrow‐Debreu equilibria and to be Pareto optimal. If investors all have linear risk tolerance with the same cautiousness parameter, then equilibria are Pareto optimal, equilibrium prices are independent of the initial wealth allocation (Gorman aggregation), and two‐fund separation holds (all investors hold the risk‐free asset and the ma
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Book chapters on the topic "Optimal sample allocation"

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Hankin, David G., Michael S. Mohr, and Ken B. Newman. "Stratified sampling." In Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.003.0005.

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In stratified sampling, the N population units are grouped into L strata, independent samples are selected from within each stratum, and unbiased estimation is achieved as a weighted average of stratum-specific estimates. Strata may be natural—pool, riffle, and run habitat unit types in a small stream—or strata may be constructed to ensure that some units from specific groups of population units will always be included in the sample. Within strata, any unbiased method of selection can be used. If SRS is used within strata, this is a stratified SRS design. Allocation of the total stratified sample of size n across the L strata can affect sampling variance of stratified estimators. Optimal allocation theory shows that optimal stratum-specific sample sizes depend on relative numbers of units in strata, and stratum-specific costs per unit of sampling and variances of y values. An ANOVA sums of squares partition can be used to show that a proportionally allocated stratified SRS strategy will outperform selection of a single SRS with mean-per-unit estimation whenever the average variation within strata is less than the finite population variance. Therefore, it is desirable to minimize variation within strata and maximize the variation in stratum means. For a variety of reasons, post-stratification, in which one large SRS is stratified after the sample has been selected, may often be a good alternative to selection of a (pre-) stratified sample.
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Hankin, David G., Michael S. Mohr, and Ken B. Newman. "Multi-phase sampling." In Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.003.0010.

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Attention is restricted to two-phase or double sampling. A large first-phase sample is used to generate a very good estimate of the mean or total of an auxiliary variable, x, which is relatively cheap to measure. Then, a second-phase sample is selected, usually from the first-phase sample, and both auxiliary and target variables are measured in selected second-phase population units. Two-phase ratio or regression estimators can be used effectively in this context. Errors of estimation reflect first-phase uncertainty in the mean or total of the auxiliary variable, and second-phase errors reflect the nature of the relation and correlation between auxiliary and target variables. Accuracy of the two-phase estimator of a proportion depends on sensitivity and specificity. Sensitivity is the probability that a unit possessing a trait (y = 1) will be correctly classified as such whenever the auxiliary variable, x, has value 1, whereas specificity is the probability that a unit not possessing a trait (y = 0) will be correctly classified as such whenever the auxiliary variable, x, has value 0. Optimal allocation results for estimation of means, totals, and proportions allow the most cost-effective allocation of total sampling effort to the first- and second-phases. In double sampling with stratification, a large first-phase sample estimates stratum weights, a second-phase sample estimates stratum means, and a stratified estimator gives an estimate of the overall population mean or total.
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Hansen, Lars Peter, and Thomas J. Sargent. "Optimal Resource Allocations." In Recursive Models of Dynamic Linear Economies. Princeton University Press, 2013. http://dx.doi.org/10.23943/princeton/9780691042770.003.0005.

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This chapter describes a planning problem that generates competitive equilibrium allocations and compares two methods for solving it. The first method uses state- and date-contingent Lagrange multipliers; the second uses dynamic programming. The first method reveals a direct connection between the Lagrange multipliers and the equilibrium prices in a competitive equilibrium to be analyzed in Chapter 7. The second method provides good algorithms for calculating both the law of motion for the optimal quantities and the Lagrange multipliers. The chapter also describes a set of MATLAB programs that solve the planning problem and represent its solution in various ways. These programs are used to solve the planning problem for six sample economies formed by choosing particular examples of the ingredients from Chapter 4.
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Hankin, David G., Michael S. Mohr, and Ken B. Newman. "Multi-stage sampling." In Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.003.0009.

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In multi-stage sampling, there are two or more stages of sampling and the simplest version, which the chapter emphasizes is called two-stage sampling. In two-stage sampling, an initial first-stage sample of n primary units (or clusters) is selected. Then, at the second stage of sampling, m <sub>i</sub> subunits are selected from the M <sub>i</sub> subunits in the selected primary units. First- and second-stage units may be selected with equal or unequal probabilities and a wide variety of estimators may be used to estimate totals within selected primary units and to estimate the total of the target variable in the finite population. Illustrative sample spaces are provided for equal sized two-stage cluster sampling with SRS selection at both stages, and for two-stage unequal size cluster sampling, with clusters selected by PPSWOR and units within clusters selected by SRS. Sampling variance is shown to originate from two sources: variation between primary unit totals or means (first-stage variance), and errors of estimation of primary units totals (second-stage variance). Topics of optimal allocation and net relative efficiency are addressed in the two-stage context with equal and unequal size clusters. General expressions for sampling variance are presented for three or more stages of sampling. The multi-stage framework can take powerful advantage of all of the concepts and sampling designs considered in previous chapters and the ecologist or natural resource scientist can apply everything he/she knows about an ecological or natural resource setting to guide development of an intelligent multi-stage sampling strategy.
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Berg, Benjamin, and Mor Harchol-Balter. "Optimal Scheduling of Parallel Jobs With Unknown Service Requirements." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7156-9.ch003.

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Large data centers composed of many servers provide the opportunity to improve performance by parallelizing jobs. However, effectively exploiting parallelism is non-trivial. For each arriving job, one must decide the number of servers on which the job is run. The goal is to determine the optimal allocation of servers to jobs that minimizes the mean response time across jobs – the average time from when a job arrives until it completes. Parallelizing a job across multiple servers reduces the response time of that individual job. However, jobs receive diminishing returns from being allocated additional servers, so allocating too many servers to a single job leads to low system efficiency. The authors consider the case where the remaining sizes of jobs are unknown to the system at every moment in time. They prove that, if all jobs follow the same speedup function, the optimal policy is EQUI, which divides servers equally among jobs. When jobs follow different speedup functions, EQUI is no longer optimal and they provide an alternate policy, GREEDY*, which performs within 1% of optimal in simulation.
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Kaya, Onur, and Sennur Ulukus. "Power Allocation for Cooperative Communications." In Cooperative Communications for Improved Wireless Network Transmission. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-665-5.ch003.

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In this chapter, we review the optimal power allocation policies for fading channels in single user and multiple access scenarios. We provide some background on cooperative communications, starting with the relay channel, and moving onto mutually cooperative systems. Then, we consider power control and user cooperation jointly, and for a fading Gaussian multiple access channel (MAC) with user cooperation, we present a channel adaptive encoding policy, which relies on block Markov superposition coding. We obtain the power allocation policies that maximize the average rates achievable by block Markov coding, subject to average power constraints. The optimal policies result in a coding scheme that is simpler than the one for a general multiple access channel with generalized feedback. This simpler coding scheme also leads to the possibility of formulating an otherwise non-concave optimization problem as a concave one. Using the perfect channel state information (CSI) available at the transmitters to adapt the powers, we demonstrate significant gains over the achievable rates for existing cooperative systems. We consider both backwards and window decoding, and show that, window decoding, which incurs less decoding delay, achieves the same sum rate as backwards decoding, when the powers are optimized.
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Chaitanya, Tumula V. K., Tho Le-Ngoc, and Erik G. Larsson. "Energy-Efficient Power Allocation for HARQ Systems." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8732-5.ch008.

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Reliability of data transmission is a fundamental problem in wireless communications. Fading in wireless channels causes the signal strength to vary at the receiver and this results in loss of data packets. To improve the reliability, automatic repeat request (ARQ) schemes were introduced. However these ARQ schemes suffer from a reduction in the throughput. To address the throughput reduction, conventional ARQ schemes were combined with forward error correction (FEC) schemes to develop hybrid-ARQ (HARQ) schemes. For improving the reliability of data transmission, HARQ schemes are included in the wireless standards like LTE, LTE-Advanced and WiMAX. Conventional HARQ systems use the same transmission power in different ARQ rounds. However this is not optimal in terms of minimizing the average energy spent for successful transmission of a data packet. In this book chapter, the recent research results related to HARQ systems are reviewed first. Next, optimal resource allocation in HARQ systems with a limit on the maximum number of allowed transmissions for a data packet is considered in the next part. Specifically, the problem of minimizing the rate-outage probability under a constraint on average energy consumption per data packet for both incremental redundancy (IR)-based and Chase combining (CC)-based HARQ systems is considered. Towards solving the optimization problems, the expressions for rate-outage probability of both IR-HARQ and CC-HARQ systems in i.i.d. Rayleigh fading channels is provided. Methods to solve the optimization problems using nonlinear optimization techniques are discussed. To reduce the complexity of finding a solution, the rate-outage probability expressions are approximated, using which, the non-convex optimization problems are converted into geometric programming problems (GPPs), for which the closed-form solutions are derived. Illustrative and analytical results show that the proposed power allocation provides significant gains in energy savings over the traditional equal power allocation transmission, and the closed-form GPP solution can provide a performance close to that of the exact method for smaller values of rate-outage probability.
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Zhao, Kun, Guantao Chen, Thomas Gift, and Guoyu Tao. "Optimization Model and Algorithm Help to Screen and Treat Sexually Transmitted Diseases." In Innovations in Data Methodologies and Computational Algorithms for Medical Applications. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0282-3.ch014.

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Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are two common sexually transmitted diseases among women in the United States. Publicly funded programs usually do not have enough money to screen and treat all patients. Therefore, the authors propose a new resource allocation model to assist clinical managers to make decisions on identifying at-risk population groups, as well as selecting a screening and treatment strategy for CT and GC patients under a fixed budget. At the same time, the authors also develop a two-step branch-and-bound algorithm tailor-made for our model. Running on real-life data, the algorithm calculates the optimal solution within a very short time. The new algorithm also improves the accuracy of an approximate solution obtained by Excel Solver. This study has shown that a resource allocation model and algorithm might have a significant impact on real clinical issues.
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Acharya, Tamaghna, and Santi P. Maity. "Power Allocation in Cognitive Radio in Energy Constrained Wireless Ad Hoc Networks." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4221-8.ch013.

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The acute scarcity of radio frequency spectrum has inspired to think of a new communication technology where the devices are expected to be able to sense and adapt to their spectral environment, thereby appearing as cognitive radios (CR) who can share opportunistically the bands assigned to primary users (PUs). At the same time, low cost, increased coverage, enhanced capacity, infrastructure-less configuration, and so forth, become the essence of future wireless networks. Although the two research fields came up independently, in due time it is observed that CR has a promising future and has excellent applications in wireless networks. To this aim, this chapter explores some scope of integration in CR and ad hoc networks (called here CRAHNETs) in some specific design perspective. First, a brief literature review on CR power allocation and energy aware routing in wireless ad hoc networks (WANETs) is done that highlights the importance for the scope of their integration. Then, power allocation in CRAHNETs with extended network lifetime is considered as an example problem. More specifically, the design problem is: given a set of paths (routes) between a pair of source (S) and destination (D) nodes in CRAHNETs, how to allocate optimal power to the source and relay nodes such that outage probability for data transmission is minimized and network lifetime is enhanced, while meeting the limits of total transmit power of CRs and interference threshold to PU simultaneously. A solution for the stated problem is proposed along with performance evaluation. A few related research problems are mentioned as future research directions.
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Fehr, Hans, and Fabian Kindermann. "Extending the OLG model." In Introduction to Computational Economics Using Fortran. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198804390.003.0011.

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In Chapter 6 we used our basic OLG model to discuss the welfare and efficiency effects of various policy reforms. Of course, we have to be cautious in drawing robust conclusions fromsuch a policy analysis. In the basic model households only decide on their intertemporal consumption allocation. Hence, public policy solely distorts the savings decision and, consequently, most of the policy reforms hardly impact on economic efficiency but only redistribute across cohorts. Our analysis could be much more instructive when decisions of economic agents are multidimensional, so that various distortions induced by public policy interact. In this chapter we therefore introduce an extended individual decision process. Households not only decide on their savings, but also on their time use. Given a specific time endowment (say a day or a year), agents can either work in the market (and earn income), go to school (and acquire human capital for future income generation), or consume leisure. Public policy may distort all of these decisions. A good policy thus has to create a balance between intertemporal and intratemporal distortions. Finally, we study the implications of lifespan uncertainty and missing annuity markets, asking how public policy can improve the allocation of resources by providing insurance against longevity risk. In this section we allow households to decide how many hours to work in each period. The remaining time is used for leisure consumption which now features in household utility. Leisure demand in each period of the life cycle strongly depends on the respective value of human capital hj, which measures the value of the time endowment in terms of labour market productivity. Hence agents may work the same number of hours, but they may be differently productive, so that they earn a different wage per time unit. Whenever the wage a household earns in the labour market is very small, the household might want to consume more leisure than the actual time endowment. In order to guarantee that the time endowment is met, we calculate a so-called shadow wage μj,s. The shadow wage is added to the regular wage of the household and calculated such that the household’s optimal decision consists in consuming the household’s total endowment of time as leisure.
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Conference papers on the topic "Optimal sample allocation"

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Dong, Guangling, Chi He, and Zhengguo Dai. "Optimal sample size allocation for integrated test scheme." In 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). IEEE, 2015. http://dx.doi.org/10.1109/civemsa.2015.7158624.

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Lin, Pin-Yi, and Kuei-Yuan Chan. "Optimal Sample Augmentation and Resource Allocation for Design With Inadequate Uncertainty Data." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70234.

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Uncertainty modeling in reliability-based design optimization problems requires a large amount of measurement data that are generally too costly in engineering practice. Instead, engineers are constantly challenged to make timely design decisions with only limited information at hand. In the literature, Bayesian binomial inference techniques have been used to estimate the reliability values of functions of uncertainties with limited samples. However, existing methods assume one sample as the entire set of measurements with one for each uncertain quantity while in reality one sample is one meas
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Sudarsanam, Nandan, Ramya Chandran, and Daniel D. Frey. "Conducting Non-Adaptive Experiments in a Live Setting: A Bayesian Approach to Determining Optimal Sample Size." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98335.

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Abstract This research studies the use of predetermined experimental plans in a live setting with a finite implementation horizon. In this context, we seek to determine the optimal experimental budget in different environments using a Bayesian framework. We derive theoretical results on the optimal allocation of resources to treatments with the objective of minimizing cumulative regret, a metric commonly used in online statistical learning. Our base case studies a setting with two treatments assuming Gaussian priors for the treatment means and noise distributions. We extend our study through a
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Zhang, Mengyi, Andrea Matta, and Arianna Alfieri. "Sample-Path Algorithm for Global Optimal Solution of Resource Allocation in Queueing Systems with Performance Constraints." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9384061.

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Jaiswal, Prateek, Harsha Honnappa, and Raghu Pasupathy. "OPTIMAL ALLOCATIONS FOR SAMPLE AVERAGE APPROXIMATION." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632258.

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Maity, Santi P., and Claude Delpha. "Optimal watermark power and host samples allocation under random gain attack." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6467331.

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Xie, Yongquan, Zude Zhou, Duc Truong Pham, et al. "A Forager Adjustment Strategy Used by the Bees Algorithm for Solving Optimization Problems in Cloud Manufacturing." In ASME 2015 International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/msec2015-9255.

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Intelligent technologies have become increasingly important in manufacturing nowadays. Optimal service management and allocation in current cloud manufacturing model are impossible without applications of appropriate intelligent tools. The Bees Algorithm (BA) is a swarm-based intelligent optimizer that provides support for smart decision-making process in manufacturing models. A novel forager adjustment strategy (FAS) is proposed in this paper to manage the forager division in the algorithm, so as to make the entire colony perform with higher efficiency. The proposed FAS based Bees Algorithm (
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Xu, Yifan, Pan Xu, Jianping Pan, and Jun Tao. "A Unified Model for the Two-stage Offline-then-Online Resource Allocation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/581.

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With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation proce
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Selmair, Maximilian, Sascha Hamzehi, and Klaus-Juergen Meier. "Evaluation Of Algorithm Performance For Simulated Square And Non-Square Logistic Assignment Problems." In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0016.

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The optimal allocation of transportation tasks to a fleet of vehicles, especially for large-scale systems of more than 20 Autonomous Mobile Robots (AMRs), remains a major challenge in logistics. Optimal in this context refers to two criteria: how close a result is to the best achievable objective value and the shortest possible computational time. Operations research has provided different methods that can be applied to solve this assignment problem. Our literature review has revealed six commonly applied methods to solve this problem. In this paper, we compared three optimal methods (Integer
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Shah, Amip, Cullen Bash, Martin Arlitt, et al. "Thermal Management Considerations for Geographically Distributed Computing Infrastructures." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22912.

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This paper discusses an approach for optimizing the infrastructure thermal performance related to a geographically distributed computing service. Beginning by modeling the total energy costs associated with cooling a distributed environment, the cooling efficiency of a service is evaluated by superposing the piecewise IT workloads that may be delivered from various locations. We find that the total service-level thermal performance can be distinct from the facility- or infrastructure-level thermal performance, which requires a different global thermal management strategy relative to that of si
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Reports on the topic "Optimal sample allocation"

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Royset, Johannes O., and Roberto Szechtman. Optimal Budget Allocation for Sample Average Approximation. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada551784.

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