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Journal articles on the topic 'Random allocation'

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Published: 4 June 2021
Last updated: 25 July 2025

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1

Akbarpour, Mohammad, and Afshin Nikzad. "Approximate Random Allocation Mechanisms." Review of Economic Studies 87, no. 6 (2020): 2473–510. http://dx.doi.org/10.1093/restud/rdz066.

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Abstract We generalize the scope of random allocation mechanisms, in which the mechanism first identifies a feasible “expected allocation” and then implements it by randomizing over nearby feasible integer allocations. The previous literature has shown that the cases in which this is possible are sharply limited. We show that if some of the feasibility constraints can be treated as goals rather than hard constraints, then, subject to weak conditions that we identify, any expected allocation that satisfies all the constraints and goals can be implemented by randomizing among nearby integer allo
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2

Budish, Eric, Yeon-Koo Che, Fuhito Kojima, and Paul Milgrom. "Designing Random Allocation Mechanisms: Theory and Applications." American Economic Review 103, no. 2 (2013): 585–623. http://dx.doi.org/10.1257/aer.103.2.585.

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Randomization is commonplace in everyday resource allocation. We generalize the theory of randomized assignment to accommodate multi-unit allocations and various real-world constraints, such as group-specific quotas (“controlled choice”) in school choice and house allocation, and scheduling and curriculum constraints in course allocation. We develop new mechanisms that are ex ante efficient and fair in these environments, and that incorporate certain non-additive substitutable preferences. We also develop a “utility guarantee” technique that limits ex post unfairness in random allocations, sup
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3

Sedgwick, P. "Random sampling versus random allocation." BMJ 343, no. 23 2 (2011): d7453. http://dx.doi.org/10.1136/bmj.d7453.

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4

Sedgwick, P. "Stratified random allocation." BMJ 346, feb08 1 (2013): f822. http://dx.doi.org/10.1136/bmj.f822.

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5

Sedgwick, P. "Random allocation I." BMJ 339, no. 18 1 (2009): b4719. http://dx.doi.org/10.1136/bmj.b4719.

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6

Sedgwick, P. "Random allocation II." BMJ 339, no. 25 2 (2009): b4924. http://dx.doi.org/10.1136/bmj.b4924.

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7

Sedgwick, P. "Random allocation III." BMJ 339, dec02 2 (2009): b5072. http://dx.doi.org/10.1136/bmj.b5072.

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8

Kang, Hyun. "Random allocation and dynamic allocation randomization." Anesthesia and Pain Medicine 12, no. 3 (2017): 201–12. http://dx.doi.org/10.17085/apm.2017.12.3.201.

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9

Sedgwick, P. "Controlled trials: allocation concealment, random allocation, and blinding." BMJ 350, may15 2 (2015): h2633. http://dx.doi.org/10.1136/bmj.h2633.

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10

Lei, Xiaoli. "Resource Sharing Algorithm of Ideological and Political Course Based on Random Forest." Mathematical Problems in Engineering 2022 (May 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/8765166.

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Three aspects of the system’s online resource distribution and application are built around subject, object, and intermediary resources. The invention relates to a method for allocating resources based on the random forest algorithm. The resource allocation process entails the following steps: constructing a mathematical model of the resource allocation process, defining a mathematical model of the resource allocation process for the target object, and designing the cost function. The training data set for random forest is constructed using the classification concept. It is based on the mathem
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11

Gani, J. "Random-allocation and urn models." Journal of Applied Probability 41, A (2004): 313–20. http://dx.doi.org/10.1239/jap/1082552207.

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We review some urn and random-allocation models, mostly using probability generating function (PGF) methods. We begin by formulating a basic problem which can be thought of as either an urn or a random-allocation model; a PGF solution to it is outlined. When the compartments in the latter model are no longer homogeneous, the multivariate PGF can still be derived, though the algebra becomes cumbersome. Some results are given for the case where there are two types of compartment and for the case where there are two types of ball. Some comments are offered on the Frobenius–Harper property of PGFs
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12

Davey Smith, George. "Random Allocation in Observational Data." Epidemiology 22, no. 4 (2011): 460–63. http://dx.doi.org/10.1097/ede.0b013e31821d0426.

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13

Gani, J. "Random-allocation and urn models." Journal of Applied Probability 41, A (2004): 313–20. http://dx.doi.org/10.1017/s0021900200112379.

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We review some urn and random-allocation models, mostly using probability generating function (PGF) methods. We begin by formulating a basic problem which can be thought of as either an urn or a random-allocation model; a PGF solution to it is outlined. When the compartments in the latter model are no longer homogeneous, the multivariate PGF can still be derived, though the algebra becomes cumbersome. Some results are given for the case where there are two types of compartment and for the case where there are two types of ball. Some comments are offered on the Frobenius–Harper property of PGFs
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14

Frieze, Alan, and Samantha Petti. "Balanced allocation through random walk." Information Processing Letters 131 (March 2018): 39–43. http://dx.doi.org/10.1016/j.ipl.2017.11.010.

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15

Han, Xiang. "A theory of fair random allocation under priorities." Theoretical Economics 19, no. 3 (2024): 1185–221. http://dx.doi.org/10.3982/te5262.

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In the allocation of indivisible objects under weak priorities, a common practice is to break the ties using a lottery and to randomize over deterministic mechanisms. Such randomizations usually lead to unfairness and inefficiency ex ante. We propose and study the concept of ex ante fairness for random allocations, extending some key results in the one‐sided and two‐sided matching markets. It is shown that the set of ex ante fair random allocations forms a complete and distributive lattice under first‐order stochastic‐dominance relations, and the agent‐optimal ex ante fair mechanism includes b
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16

Featherstone, K., and J. L. Donovan. "Random allocation or allocation at random? Patients' perspectives of participation in a randomised controlled trial." BMJ 317, no. 7167 (1998): 1177–80. http://dx.doi.org/10.1136/bmj.317.7167.1177.

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17

Li, Xiaohu, and Yinping You. "Permutation Monotone Functions of Random Vectors with Applications in Financial and Actuarial Risk Management." Advances in Applied Probability 47, no. 1 (2015): 270–91. http://dx.doi.org/10.1239/aap/1427814591.

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In this paper we develop two permutation theorems on argument increasing functions of a multivariate random vector and a real parameter vector. We use the unified approach of our two theorems to provide some important theoretical results on the capital allocation in actuarial science, the deductible and upper limit allocations in insurance policy, and portfolio allocation in financial engineering. Our results successfully improve or extend the corresponding works in the literature.
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18

Li, Xiaohu, and Yinping You. "Permutation Monotone Functions of Random Vectors with Applications in Financial and Actuarial Risk Management." Advances in Applied Probability 47, no. 01 (2015): 270–91. http://dx.doi.org/10.1017/s0001867800007801.

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In this paper we develop two permutation theorems on argument increasing functions of a multivariate random vector and a real parameter vector. We use the unified approach of our two theorems to provide some important theoretical results on the capital allocation in actuarial science, the deductible and upper limit allocations in insurance policy, and portfolio allocation in financial engineering. Our results successfully improve or extend the corresponding works in the literature.
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19

Yen-Wen Chen, Yen-Wen Chen, and Ji-Zheng You Yen-Wen Chen. "Effective Radio Resource Allocation for IoT Random Access by Using Reinforcement Learning." 網際網路技術學刊 23, no. 5 (2022): 1069–75. http://dx.doi.org/10.53106/160792642022092305015.

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<p>Emerging intelligent and highly interactive services result in the mass deployment of internet of things (IoT) devices. They are dominating wireless communication networks compared to human-held devices. Random access performance is one of the most critical issues in providing quick responses to various IoT services. In addition to the anchor carrier, the non-anchor carrier can be flexibly allocated to support the random access procedure in release 14 of the 3rd generation partnership project. However, arranging more non-anchor carriers for the use of random access will squeeze the da
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20

Marinescu, Marian V., Taraneh Sowlati, and Thomas C. Maness. "The development of a timber allocation model using data envelopment analysis." Canadian Journal of Forest Research 35, no. 10 (2005): 2304–15. http://dx.doi.org/10.1139/x05-141.

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A timber allocation model using data envelopment analysis (DEA) was developed to test the capability of DEA to assist with multicriteria timber allocation decisions. The resulting DEA Timber Allocation model is able to allocate forest stands, referred to as stewardship units, to different forest products companies without the need for weighing or prioritizing the allocation criteria. The allocation procedure was demonstrated in a case considering two allocation criteria: profit and employment. The allocation generated by the model was compared with random, profit-based, and employment-based al
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21

Kim, Jeehyoung, and Wonshik Shin. "How to Do Random Allocation (Randomization)." Clinics in Orthopedic Surgery 6, no. 1 (2014): 103. http://dx.doi.org/10.4055/cios.2014.6.1.103.

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22

Rogers, Jennifer. "How “random” is Ryanair's seating allocation?" Significance 14, no. 5 (2017): 12–13. http://dx.doi.org/10.1111/j.1740-9713.2017.01069.x.

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23

Egbewale, Bolaji Emmanuel. "Random Allocation in Controlled Clinical Trials: A Review." Journal of Pharmacy & Pharmaceutical Sciences 17, no. 2 (2014): 248. http://dx.doi.org/10.18433/j3sw36.

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PURPOSE: An allocation strategy that allows for chance placement of participants to study groups is crucial to the experimental nature of randomised controlled trials. Following decades of the discovery of randomisation considerable erroneous opinion and misrepresentations of its concept both in principle and practice still exists. In some circles, opinions are also divided on the strength and weaknesses of each of the random allocation strategies. This review provides an update on various random allocation techniques so as to correct existing misconceptions on this all important procedure. ME
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24

Fives, Allyn. "The Role of Random Allocation in Randomized Controlled Trials: Distinguishing Selection Bias from Baseline Imbalance." Journal of MultiDisciplinary Evaluation 9, no. 20 (2013): 33–42. http://dx.doi.org/10.56645/jmde.v9i20.371.

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Background: This paper addresses one threat to the internal validity of a randomized controlled trial (RCT), selection bias. Many authors argue that random allocation is used to ensure baseline equality between study conditions in a given study and that statistically significant differences at pretest mean that randomisation has failed. Purpose: The purpose of this study was to clarify the role of random allocation in an RCT study. Is the role of random allocation to protect against selection bias? And does it have a further role, namely to ensure baseline equality and the absence of statistic
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25

Bambos, Nicholas, and George Michailidis. "Queueing and scheduling in random environments." Advances in Applied Probability 36, no. 1 (2004): 293–317. http://dx.doi.org/10.1239/aap/1077134474.

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We consider a processing system, composed of several parallel queues and a processor, which operates in a time-varying environment that fluctuates between various states or modes. The service rate at each queue depends on the processor bandwidth allocated to it, as well as the environment mode. Each queue is driven by a job traffic flow, which may also depend on the environment mode. Dynamic processor scheduling policies are investigated for maximizing the system throughput, by adapting to queue backlogs and the environment mode. We show that allocating the processor bandwidth to the queues, s
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26

Bambos, Nicholas, and George Michailidis. "Queueing and scheduling in random environments." Advances in Applied Probability 36, no. 01 (2004): 293–317. http://dx.doi.org/10.1017/s0001867800012970.

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We consider a processing system, composed of several parallel queues and a processor, which operates in a time-varying environment that fluctuates between various states or modes. The service rate at each queue depends on the processor bandwidth allocated to it, as well as the environment mode. Each queue is driven by a job traffic flow, which may also depend on the environment mode. Dynamic processor scheduling policies are investigated for maximizing the system throughput, by adapting to queue backlogs and the environment mode. We show that allocating the processor bandwidth to the queues, s
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27

Tsitsiashvili, G. S. "Moments of Random Allocation Processes Reaching a Boundary." Journal of Applied Probability 44, no. 4 (2007): 990–95. http://dx.doi.org/10.1239/jap/1197908819.

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In this paper we develop some results presented by Gani (2004), deriving moments for random allocation processes. These moments correspond to the allocation processes reaching some domain boundary. Exact formulae for means, variances, and probability generating functions as well as some asymptotic formulae for moments of random allocation processes are obtained. A special choice of the asymptotics and of the domain allows us to reduce a complicated numerical procedure to a simple asymptotic one.
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28

Tsitsiashvili, G. S. "Moments of Random Allocation Processes Reaching a Boundary." Journal of Applied Probability 44, no. 04 (2007): 990–95. http://dx.doi.org/10.1017/s0021900200003685.

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In this paper we develop some results presented by Gani (2004), deriving moments for random allocation processes. These moments correspond to the allocation processes reaching some domain boundary. Exact formulae for means, variances, and probability generating functions as well as some asymptotic formulae for moments of random allocation processes are obtained. A special choice of the asymptotics and of the domain allows us to reduce a complicated numerical procedure to a simple asymptotic one.
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29

LIN, Huifa, Koji ISHIBASHI, Won-Yong SHIN, and Takeo FUJII. "Decentralized Multilevel Power Allocation for Random Access." IEICE Transactions on Communications E98.B, no. 10 (2015): 1978–87. http://dx.doi.org/10.1587/transcom.e98.b.1978.

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30

Harris, Bernard. "Poisson limits for generalized random allocation problems." Statistics & Probability Letters 8, no. 2 (1989): 123–27. http://dx.doi.org/10.1016/0167-7152(89)90004-7.

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31

Moulin, Hervé. "The proportional random allocation of indivisible units." Social Choice and Welfare 19, no. 2 (2002): 381–413. http://dx.doi.org/10.1007/s003550100118.

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32

Nuttall, Christopher. "The Home Office and Random Allocation Experiments." Evaluation Review 27, no. 3 (2003): 267–89. http://dx.doi.org/10.1177/0193841x03027003004.

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33

Afacan, Mustafa Oǧuz. "The object allocation problem with random priorities." Games and Economic Behavior 110 (July 2018): 71–89. http://dx.doi.org/10.1016/j.geb.2018.03.010.

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34

Dong, Yanling, Shufen Zhang, Jingcheng Xu, Haoshi Wang, and Jiqiang Liu. "Random Forest Algorithm Based on Linear Privacy Budget Allocation." Journal of Database Management 33, no. 2 (2022): 1–19. http://dx.doi.org/10.4018/jdm.309413.

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In the era of big data with exponential growth in data volume, how to reduce data security issues such as data leakage caused by machine learning is a hot area of recent research. The existing privacy budget allocation strategies are usually only suitable for data applications in specific spaces and cannot meet users' personalized needs for privacy budget allocation. Therefore, a linear privacy budget allocation strategy is proposed. The strategy assigns each layer a linearly increasing privacy budget from the root of the decision tree to the bottom by adjusting the coefficient or constant ter
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35

Dillenberger, David, and Uzi Segal. "Allocation Mechanisms without Reduction." American Economic Review: Insights 3, no. 4 (2021): 455–70. http://dx.doi.org/10.1257/aeri.20200728.

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We study a simple variant of the house allocation problem (one-sided matching). We demonstrate that agents with recursive preferences may systematically prefer one allocation mechanism to the other, even among mechanisms that are considered to be the same in standard models, in the sense that they induce the same probability distribution over successful matchings. Using this, we propose a new priority groups mechanism and provide conditions under which it is preferred to two popular mechanisms, random top cycle and random serial dictatorship. (JEL C78, D44, D82)
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36

Singh, Harshinder, and Neeraj Misra. "On redundancy allocations in systems." Journal of Applied Probability 31, no. 4 (1994): 1004–14. http://dx.doi.org/10.2307/3215324.

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Allocation of a redundant component in a system in order to optimize, in some sense, the lifetime of the system is an important problem in reliability theory, having practical applications. Consider a series system consisting of two components (say C1 and C2), having independent random lifetimes X1 and X2, and suppose a component C having random lifetime X (independent of X1 and X2) is available for active redundancy with one of the components. Let U1 = min(max(X1, X), X2) and U2 = min(X1, max(X2, X)), so that U1 (U2) denote the lifetime of a system obtained by allocating C to C1 (C2). We cons
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37

Mathew, Olayiwola Olaniyi. "Efficiency of Neyman Allocation Procedure over other Allocation Procedures in Stratified Random Sampling." American Journal of Theoretical and Applied Statistics 2, no. 5 (2013): 122. http://dx.doi.org/10.11648/j.ajtas.20130205.12.

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38

Singh, Harshinder, and Neeraj Misra. "On redundancy allocations in systems." Journal of Applied Probability 31, no. 04 (1994): 1004–14. http://dx.doi.org/10.1017/s0021900200099526.

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Allocation of a redundant component in a system in order to optimize, in some sense, the lifetime of the system is an important problem in reliability theory, having practical applications. Consider a series system consisting of two components (say C 1 and C 2), having independent random lifetimes X 1 and X 2, and suppose a component C having random lifetime X (independent of X 1 and X 2) is available for active redundancy with one of the components. Let U 1 = min(max(X 1, X), X 2) and U 2 = min(X 1, max(X 2, X)), so that U 1 (U 2) denote the lifetime of a system obtained by allocating C to C
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39

Kaminska, Olena, and Peter Lynn. "The Implications of Alternative Allocation Criteria in Adaptive Design for Panel Surveys." Journal of Official Statistics 33, no. 3 (2017): 781–99. http://dx.doi.org/10.1515/jos-2017-0036.

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AbstractAdaptive survey designs can be used to allocate sample elements to alternative data collection protocols in order to achieve a desired balance between some quality measure and survey costs. We compare four alternative methods for allocating sample elements to one of two data collection protocols. The methods differ in terms of the quality measure that they aim to optimize: response rate, R-indicator, coefficient of variation of the participation propensities, or effective sample size. Costs are also compared for a range of sample sizes. The data collection protocols considered are CAPI
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40

Daley, D. J., and J. Gani. "A random allocation model for carrier-borne epidemics." Journal of Applied Probability 30, no. 4 (1993): 751–65. http://dx.doi.org/10.2307/3214510.

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A carrier-borne epidemic is considered in which the carriers, subject to a death process, infect susceptibles by random allocation rather than the classical homogeneous mixing process. An explicit solution for the probability generating function (p.g.f.) of the process is obtained, and a probabilistic analysis of carrier models provided. The sizes and durations of the random allocation and classical carrier epidemics are compared. The strongest comparisons concern sample path results based on the probabilistic analysis; this also gives a sounder basis for computational work.
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41

Righter, Rhonda. "A Resource Allocation Problem in a Random Environment." Operations Research 37, no. 2 (1989): 329–38. http://dx.doi.org/10.1287/opre.37.2.329.

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42

Daley, D. J., and J. Gani. "A random allocation model for carrier-borne epidemics." Journal of Applied Probability 30, no. 04 (1993): 751–65. http://dx.doi.org/10.1017/s0021900200044545.

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A carrier-borne epidemic is considered in which the carriers, subject to a death process, infect susceptibles by random allocation rather than the classical homogeneous mixing process. An explicit solution for the probability generating function (p.g.f.) of the process is obtained, and a probabilistic analysis of carrier models provided. The sizes and durations of the random allocation and classical carrier epidemics are compared. The strongest comparisons concern sample path results based on the probabilistic analysis; this also gives a sounder basis for computational work.
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43

Ayhan, H. Öztaş, and M. Qamar Islam. "Sample Design and Allocation for Random Digit Dialling." Quality & Quantity 39, no. 5 (2005): 625–41. http://dx.doi.org/10.1007/s11135-005-3152-4.

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44

Ekici, Özgün. "Random mechanisms for house allocation with existing tenants." Journal of Mathematical Economics 89 (August 2020): 53–65. http://dx.doi.org/10.1016/j.jmateco.2020.05.003.

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45

Kasturi, Jyotsna, John G. Geisler, Jianying Liu, Thomas Kirchner, Dhammika Amaratunga, and Mariusz Lubomirski. "IRINI: Random group allocation of multiple prognostic factors." Contemporary Clinical Trials 32, no. 3 (2011): 372–81. http://dx.doi.org/10.1016/j.cct.2010.12.009.

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46

L'vovskii, Ya M. "Random allocation for a class of polya plans." Journal of Soviet Mathematics 56, no. 3 (1991): 2481–88. http://dx.doi.org/10.1007/bf01096118.

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47

Pourmiri, Ali. "Balanced allocation on graphs: A random walk approach." Random Structures & Algorithms 55, no. 4 (2019): 980–1009. http://dx.doi.org/10.1002/rsa.20875.

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48

Chakrabarti, Soumen. "Random allocation of jobs with weights and precedence." Theoretical Computer Science 162, no. 2 (1996): 341–49. http://dx.doi.org/10.1016/0304-3975(96)00036-9.

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49

Senn, S. J. "Covariate imbalance and random allocation in clinical trials." Statistics in Medicine 8, no. 4 (1989): 467–75. http://dx.doi.org/10.1002/sim.4780080410.

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50

Vanni, Fabio, Asmerilda Hitaj, and Elisa Mastrogiacomo. "Enhancing Portfolio Allocation: A Random Matrix Theory Perspective." Mathematics 12, no. 9 (2024): 1389. http://dx.doi.org/10.3390/math12091389.

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This paper explores the application of Random Matrix Theory (RMT) as a methodological enhancement for portfolio selection within financial markets. Traditional approaches to portfolio optimization often rely on historical estimates of correlation matrices, which are particularly susceptible to instabilities. To address this challenge, we combine a data preprocessing technique based on the Hilbert transformation of returns with RMT to refine the accuracy and robustness of correlation matrix estimation. By comparing empirical correlations with those generated through RMT, we reveal non-random pr
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