Journal articles: 'Sample size allocation'

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MENZEFRICKE, ULRICH, and WALLY SMIELIAUSKAS. "On sample size allocation in auditing." Contemporary Accounting Research 4, no. 2 (1988): 314–36. http://dx.doi.org/10.1111/j.1911-3846.1988.tb00669.x.

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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 single-mode and web-CAPI sequential mixed-mode. We use data from a large experiment with random allocation to one of these two protocols. For each allocation method we predict outcomes in terms of several quality measures and costs. Although allocating the whole sample to single-mode CAPI produces a higher response rate than allocating the whole sample to the mixed-mode protocol, we find that two of the targeted allocations achieve a better response rate than single-mode CAPI at a lower cost. We also find that all four of the targeted designs out-perform both single-protocol designs in terms of representativity and effective sample size. For all but the smallest sample sizes, the adaptive designs bring cost savings relative to CAPI-only, though these are fairly modest in magnitude.

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Shao, Shuangshuang, Huan Zhang, Manman Fan, et al. "Spatial variability-based sample size allocation for stratified sampling." CATENA 206 (November 2021): 105509. http://dx.doi.org/10.1016/j.catena.2021.105509.

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Otis, David L. "Optimum Sample Size Allocation for Wood Duck Banding Studies." Journal of Wildlife Management 58, no. 1 (1994): 114. http://dx.doi.org/10.2307/3809557.

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Uesaka, Hiroyuki. "Sample Size Allocation to Regions in a Multiregional Trial." Journal of Biopharmaceutical Statistics 19, no. 4 (2009): 580–94. http://dx.doi.org/10.1080/10543400902963185.

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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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Pandey, Rajiv. "Sample allocation decisions for program evaluation: a case of multiplicative effect on units due to the developmental initiatives carried out in a phased manner." Folia Forestalia Polonica, Series A - Forestry 52(2) (July 1, 2010): 83–88. https://doi.org/10.5281/zenodo.30815.

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Evaluation is primarily a measure of the effects and elaboration on the current performance of a program. It facilitates identification and choice of the ways to achieve the intended program objectives. The evaluation includes selection of samples, as well as collection and analysis of data. This paper discusses the issue of the allocation of samples for the population for which programs have been implemented in a phased manner. This needs special attention due to the temporal impact of allocations on the successive units. A method of proportional allocation has been proposed for the estimation of the sample size in different phases under the assumption that the impact of the latter phase is a multiplicative product of constant factor and the former phase.

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Wang, Zhihua, Yu Han, Wenbo Li, Gen Liu, and Chengrui Liu. "An information fusion reliability allocation method considering limited sample size circumstances." Quality and Reliability Engineering International 37, no. 5 (2021): 1863–74. http://dx.doi.org/10.1002/qre.2832.

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Spurrier, John D., and Azhar Nizam. "Sample Size Allocation for Simultaneous Inference in Comparison with Control Experiments." Journal of the American Statistical Association 85, no. 409 (1990): 181–86. http://dx.doi.org/10.1080/01621459.1990.10475323.

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Gabler, Siegfried, Matthias Ganninger, and Ralf Münnich. "Optimal allocation of the sample size to strata under box constraints." Metrika 75, no. 2 (2010): 151–61. http://dx.doi.org/10.1007/s00184-010-0319-3.

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Jain, R. K. "Unequal sample size allocation to optimal design for binomial logistic models." Statistische Hefte 28, no. 1 (1987): 285–90. http://dx.doi.org/10.1007/bf02932608.

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Seto, Mayumi, and Koichiro Uriu. "Sample Size Allocation for Food Item Radiation Monitoring and Safety Inspection." Risk Analysis 35, no. 3 (2014): 409–22. http://dx.doi.org/10.1111/risa.12276.

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Yue, Hong Bo, and Yi Min Shi. "Optimal Sample Size Allocation for Multi-Level Stress Testing under Progressive Hybrid Interval Censoring." Applied Mechanics and Materials 423-426 (September 2013): 2423–26. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2423.

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In this paper, we discuss the optimal allocation problem in a multi-level stress test with progressive hybrid interval censoring and Weibull regression model. We derive the maximum likelihood estimators and their asymptotic variance–covariance matrix through the Fisher information. Four optimality criteria are used to discuss the optimal allocation problem. Finally, an example is provided to illustrate the proposed design.

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Oktaviani, Yolanda, and Henny Wirianata. "PENGARUH STRUKTUR MODAL, ALOKASI PAJAK, DAN UKURAN PERUSAHAAN TERHADAP EARNING RESPONSE COEFFICIENT (ERC) PADA INDUSTRI PERKEBUNAN." Jurnal Revenue : Jurnal Ilmiah Akuntansi 5, no. 1 (2024): 794–816. http://dx.doi.org/10.46306/rev.v5i1.494.

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This research aims to analyze the influence of Capital Structure, Tax Allocation, and Company Size on the Earning Response Coefficient (ERC). The population used in this research is plantation sector companies listed on the Indonesia Stock Exchange (BEI) for the 2018 - 2022 period. The sample in this study was determined using a purposive sampling method and came from 22 plantation companies, so the total The sample amounted to 110 samples. Data processing was carried out using the multiple linear regression method with the help of SPSS software version 20. The results of the study showed that partially the Capital Structure variable had a negative and significant effect on the ERC, the Tax Allocation variable had a positive and significant effect on the ERC and the Company Size variable has a negative and significant effect on the ERC. The research results also show that Capital Structure, Tax Allocation, and Company Size simultaneously have a significant effect on the ERC

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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 ENBS across all dimensions of the design space is required to identify the optimal sample sizes and allocations within such a research portfolio. This is illustrated through a simple example of a decision model of zanamivir for the treatment of influenza. The possible study designs include: 1) a single trial of all the parameters, 2) a clinical trial providing evidence only on clinical endpoints, 3) an epidemiological study of natural history of disease, and 4) a survey of quality of life. The possible combinations, samples sizes, and allocation between trial arms are evaluated over a range of cost-effectiveness thresholds. The computational challenges are addressed by implementing optimization algorithms to search the ENBS surface more efficiently over such large dimensions.

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Wagner, John E., and Stephen V. Stehman. "Optimizing sample size allocation to strata for estimating area and map accuracy." Remote Sensing of Environment 168 (October 2015): 126–33. http://dx.doi.org/10.1016/j.rse.2015.06.027.

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Shieh, Gwowen, and Show-Li Jan. "Optimal sample size allocation for Welch’s test in one-way heteroscedastic ANOVA." Behavior Research Methods 47, no. 2 (2014): 374–83. http://dx.doi.org/10.3758/s13428-014-0477-8.

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Guo, Jiin-Huarng, and Wei-Ming Luh. "Efficient sample size allocation with cost constraints for heterogeneous-variance group comparison." Journal of Applied Statistics 40, no. 12 (2013): 2549–63. http://dx.doi.org/10.1080/02664763.2013.819417.

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Mukerjee, Rahul, and T. J. Rao. "On a Problem of Allocation of Sample Size in Stratified Random Sampling." Biometrical Journal 27, no. 3 (1985): 327–31. http://dx.doi.org/10.1002/bimj.4710270309.

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Bouza-Herrera, Carlos N., Sira M. Allende-Alonso, Gajendra K. Vishwakarma, and Neha Singh. "Estimation of optimum sample size allocation: An illustration with body mass index for evaluating the effect of a dietetic supplement." International Journal of Biomathematics 12, no. 08 (2019): 1950086. http://dx.doi.org/10.1142/s1793524519500864.

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In many medical researches, it is needed to determine the optimal sample size allocation in a heterogeneous population. This paper proposes the algorithm for optimal sample size allocation. We consider the optimal allocation problem as an optimization problem and the solution is obtained by using Bisection, Secant, Regula–Falsi and other numerical methods. The performance of the algorithm for different numerical methods are analyzed and evaluated in terms of computing time, number of iterations and gain in accuracy using stratification. The efficacy of algorithm is evaluated for the response in terms of body mass index (BMI) to the dietetic supplement with diabetes mellitus, HIV/AIDS and cancer post-operatory recovery patients.

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AHMAD, ABRAR, QUAZZAFI RABBANI, and A. H. ANSARI. "DETERMINATION OF OPTIMUM SAMPLE SIZE AND VARIANCE IN MULTIVARIATE STRATIFIED SAMPLING WITH NON-LINEAR TIME FUNCTION." Journal of Science and Arts 23, no. 2 (2023): 513–24. http://dx.doi.org/10.46939/j.sci.arts-23.2-a18.

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Stratified sample size which determined in Stratified Random Sampling (SRS) with the help of different allocation techniques. The multivariate stratified sampling with linear or nonlinear time function can be optimized using generalized model of time function. After formulation as generalized model with time function (linear or nonlinear), in which the information given that there is only one varying time (time of transportation) and different time involved are constants or insignificant. The formulated generalized model was solved using different techniques but in present paper we use Lagrange multiplier technique to get the optimal solution (i.e. by minimizing the variance) with the involved time constraints. The generalized model with time of transportation is derived under optimum allocation and other involved time is fixed, when the sample size and its variances for a given time is unknown, then different allocation techniques are required for proportionality constant and a given time.

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Park, Young Woong. "MILP Models for Complex System Reliability Redundancy Allocation with Mixed Components." INFORMS Journal on Computing 32, no. 3 (2020): 600–619. http://dx.doi.org/10.1287/ijoc.2019.0895.

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The redundancy allocation problem (RAP) aims to find an optimal allocation of redundant components subject to resource constraints. In this paper, mixed integer linear programming (MILP) models and MILP-based algorithms are proposed for complex system reliability redundancy allocation problem with mixed components, where the system have bridges or interconnecting subsystems and each subsystem can have mixed types of components. Unlike the other algorithms in the literature, the proposed MILP models view the problem from a different point of view and approximate the nonconvex nonlinear system reliability function of a complex system using random samples. The solution to the MILP converges to the optimal solution of the original problem as sample size increases. In addition, data aggregation-based algorithms are proposed to improve the solution time and quality based on the proposed MILP models. A computational experiment shows that the proposed models and algorithms converge to the optimal or best-known solution as sample size increases. The proposed algorithms outperform popular metaheuristic algorithms in the literature.

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Barron, Martin, Michael Davern, Robert Montgomery, et al. "Using Auxiliary Sample Frame Information for Optimum Sampling of Rare Populations." Journal of Official Statistics 31, no. 4 (2015): 545–57. http://dx.doi.org/10.1515/jos-2015-0034.

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Abstract We investigate disproportionate stratified sampling as a possibly efficient method of surveying members of a rare domain in circumstances in which there is no acceptable list of members. In this work, we assume that information is available at the sampling stage to stratify the general-population sampling frame into high- and low-density strata. Under a fixed constraint on the variance of the estimator of the domain mean, we make the optimum allocation of sample size to the several strata and show that, in comparison to proportional allocation, the optimum allocation requires (a) a smaller total sample size but (b) a larger number of interviews of members of the rare domain. We illustrate the methods using information about American consumers maintained by market-research companies. Such companies are able to develop lists of households that are thought to have a defined attribute of interest, such as at least one resident in a user-specified age range. The lists are imperfect, with false positives and negatives. We apply an age-targeted list to the National Immunization Survey (NIS), conducted by the Centers for Disease Control and Prevention, which targets the relatively rare population of children age 19–35 months. The age-targeted list comprises the high-density stratum and the rest of the survey’s sampling frame comprises the low-density stratum. Given the optimum allocation, we demonstrate potential cost savings for the NIS in excess of ten percent.

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Fairley, Michael, Lauren E. Cipriano, and Jeremy D. Goldhaber-Fiebert. "Optimal Allocation of Research Funds under a Budget Constraint." Medical Decision Making 40, no. 6 (2020): 797–814. http://dx.doi.org/10.1177/0272989x20944875.

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Purpose. Health economic evaluations that include the expected value of sample information support implementation decisions as well as decisions about further research. However, just as decision makers must consider portfolios of implementation spending, they must also identify the optimal portfolio of research investments. Methods. Under a fixed research budget, a decision maker determines which studies to fund; additional budget allocated to one study to increase the study sample size implies less budget available to collect information to reduce decision uncertainty in other implementation decisions. We employ a budget-constrained portfolio optimization framework in which the decisions are whether to invest in a study and at what sample size. The objective is to maximize the sum of the studies’ population expected net benefit of sampling (ENBS). We show how to determine the optimal research portfolio and study-specific levels of investment. We demonstrate our framework with a stylized example to illustrate solution features and a real-world application using 6 published cost-effectiveness analyses. Results. Among the studies selected for nonzero investment, the optimal sample size occurs at the point at which the marginal population ENBS divided by the marginal cost of additional sampling is the same for all studies. Compared with standard ENBS optimization without a research budget constraint, optimal budget-constrained sample sizes are typically smaller but allow more studies to be funded. Conclusions. The budget constraint for research studies directly implies that the optimal sample size for additional research is not the point at which the ENBS is maximized for individual studies. A portfolio optimization approach can yield higher total ENBS. Ultimately, there is a maximum willingness to pay for incremental information that determines optimal sample sizes.

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Guo, Jiin-Huarng, and Wei-Ming Luh. "Optimum sample size allocation to minimize cost or maximize power for the two-sample trimmed mean test." British Journal of Mathematical and Statistical Psychology 62, no. 2 (2009): 283–98. http://dx.doi.org/10.1348/000711007x267289.

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Saville, Benjamin R., Yong S. Kim, and Gary G. Koch. "Graphical displays for clarifying how allocation ratio affects total sample size for the two sample logrank test." Pharmaceutical Statistics 10, no. 1 (2011): 40–44. http://dx.doi.org/10.1002/pst.407.

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Friede, T., and M. Kieser. "Sample Size Reassessment in Non-inferiority Trials." Methods of Information in Medicine 50, no. 03 (2011): 237–43. http://dx.doi.org/10.3414/me09-01-0063.

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SummaryObjectives: Analysis of covariance (ANCOVA) is widely applied in practice and its use is recommended by regulatory guidelines. However, the required sample size for ANCOVA depends on parameters that are usually uncertain in the planning phase of a study. Sample size recalculation within the internal pilot study design allows to cope with this problem. From a regulatory viewpoint it is preferable that the treatment group allocation remains masked and that the type I error is controlled at the specified significance level. The characteristics of blinded sample size reassessment for ANCOVA in non-inferiority studies have not been investigated yet. We propose an appropriate method and evaluate its performance.Methods: In a simulation study, the characteristics of the proposed method with respect to type I error rate, power and sample size are investigated. It is illustrated by a clinical trial example how strict control of the significance level can be achieved.Results: A slight excess of the type I error rate beyond the nominal significance level was observed. The extent of exceedance increases with increasing non-inferiority margin and increasing correlation between outcome and covariate. The procedure assures the desired power over a wide range of scenarios even if nuisance parameters affecting the sample size are initially mis-specified.Conclusions: The proposed blinded sample size recalculation procedure protects from insufficient sample sizes due to incorrect assumptions about nuisance parameters in the planning phase. The original procedure may lead to an elevated type I error rate, but methods are available to control the nominal significance level.

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Méndez, M., and J. R. Obeso. "Size-dependent reproductive and vegetative allocation in Arum italicum (Araceae)." Canadian Journal of Botany 71, no. 2 (1993): 309–14. http://dx.doi.org/10.1139/b93-032.

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The proportional allocation of plant total resources for growth, reproduction, vegetative propagation, and the balance between them were examined in Arum italicum. A minimum threshold dry mass (2.5 g) was found in this species before reproduction could occur, but above 10 g of dry mass, all individuals in a sample of 151 produced at least one inflorescence. Resource allocation for vegetative growth, sexual reproduction, and vegetative propagation significantly increased as dry mass of the plant increased. Increases in plant size resulted in increased proportional allocation to sexual reproduction, and relative decreases in both vegetative growth and vegetative propagation. Mass ratios between sexual reproductive structures and new tuber, and between sexual reproductive structures and organs of clonal growth increased with plant size. Allocation of resources to reproduction occurred at the expense of vegetative growth. In reproductive plants, the cost of reproduction, measured as relative reduction in vegetative growth was approximately 24% and was estimated by comparing growth in nonreproductive plants. Key words: Arum italicum, Araceae, cost of reproduction, reproductive allocation, vegetative growth, vegetative propagation.

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Yu, Xiao. "Optimal Allocation of Sample Size in Stratified Sampling with Costs Being Hybrid Variables." Journal of Information and Computational Science 10, no. 14 (2013): 4573–80. http://dx.doi.org/10.12733/jics20102166.

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Perri, Pier Francesco, María del Mar Rueda García, and Beatriz Cobo Rodríguez. "Multiple sensitive estimation and optimal sample size allocation in the item sum technique." Biometrical Journal 60, no. 1 (2017): 155–73. http://dx.doi.org/10.1002/bimj.201700021.

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Hsieh, F. Y. "Comparing sample size formulae for trials with unbalanced allocation using the logrank test." Statistics in Medicine 11, no. 8 (1992): 1091–98. http://dx.doi.org/10.1002/sim.4780110810.

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Zain, Muhammad Fadhil, and Deviani Deviani. "Pengaruh Alokasi Dana Desa terhadap Pertumbuhan Ekonomi: Studi Empiris pada Daerah Tertinggal di Indonesia pada Tahun 2019-2020." JURNAL EKSPLORASI AKUNTANSI 6, no. 2 (2024): 517–26. http://dx.doi.org/10.24036/jea.v6i2.1052.

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This research aims to provide empirical evidence regarding the influence of village fund allocation on economic growth in underdeveloped regions in Indonesia in 2019-2020. This research uses quantitative descriptive methods and is grouped into causative research. The population in this research is the allocation of village funds to disadvantaged areas in accordance with Presidential Decree Number 63 of 2020 concerning the Determination of Disadvantaged Regions for 2020-2024, which consists of 62 districts. The sampling technique in this study used a saturated sample technique, where the number of samples in this study was 62 samples with a total sample size of 124 samples. The type of data used is secondary data obtained from the recapitulation of the 2019-2020 BPS Indonesia survey report. The data analysis method uses simple linear regression analysis. The results of this research show that village fund allocation has a positive and significant influence on the economic growth of underdeveloped regions in Indonesia in 2019-2020.

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Cramer, Laura D., and Barbara S. Hawkins. "P22 Reporting of treatment allocation methods and determinants of sample size in multicenter trials." Controlled Clinical Trials 16, no. 3 (1995): 84S. http://dx.doi.org/10.1016/0197-2456(95)90502-v.

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Maryati, Maryati, Nir Setyo Wahdi, and Aprih Santoso. "Pengaruh PAD, DAU, DAK, Luas Daerah Terhadap Belanja Modal di kota/kabupaten se Jawa Tengah." Journal of Business and Economics Research (JBE) 3, no. 2 (2022): 125–33. http://dx.doi.org/10.47065/jbe.v3i2.1677.

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This study to analyze and analyze the original Regional Funds, General Allocation Funds, Specific Allocation Funds, and Area Size for Capital Expenditures in cities / districts in Central Java in 2017-2021. The data used are secondary data using a sample of 175 reports from APBD of Cities / regencies throughout Central Java in 2017-2021. This Study uses a census method, namely sampling method by making all participation into a sample. The variables used are regional income (PAD), general allocation funds (DAU), special allocation funds (DAK), and area as independent variables, while capital expenditure as the dependent variable. Data analysis was performed by multiple linear regression analysis using the SPSS 26 program. The results of the study show variable local revenue (PAD) of capital expenditure, will the general allocation funds (DAU), special allocation funds (DAK) and a broad area is not related to the capital expenditure budget allocation.

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Ullah, Atta, Javid Shabbir, Zawar Hussain, and Bander Al-Zahrani. "Estimation of Finite Population Mean in Multivariate Stratified Sampling under Cost Function Using Goal Programming." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/686579.

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In practical utilization of stratified random sampling scheme, the investigator meets a problem to select a sample that maximizes the precision of a finite population mean under cost constraint. An allocation of sample size becomes complicated when more than one characteristic is observed from each selected unit in a sample. In many real life situations, a linear cost function of a sample sizenhis not a good approximation to actual cost of sample survey when traveling cost between selected units in a stratum is significant. In this paper, sample allocation problem in multivariate stratified random sampling with proposed cost function is formulated in integer nonlinear multiobjective mathematical programming. A solution procedure is proposed using extended lexicographic goal programming approach. A numerical example is presented to illustrate the computational details and to compare the efficiency of proposed compromise allocation.

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Guo, Jiin-Huarng, and Wei-Ming Luh. "Sample Size Calculations for Testing Equivalence of Two Exponential Distributions With Right Censoring." Methodology 13, no. 4 (2017): 144–56. http://dx.doi.org/10.1027/1614-2241/a000139.

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Abstract. The present study considered two independent exponential distributions with the hypothesis for testing equivalence of lifetime means or failure rates, and aimed to determine the required uncensored sample size based on power, sampling cost, and censoring proportion simultaneously in the case of right censoring. Approximate sample size formulas with an iterative procedure were proposed and an uncensored sample size allocation ratio was derived to minimize the total cost for a designated power or maximize statistical power for a limit cost. R codes are provided for easy application. The proposed methods are validated in terms of Type I errors and statistical power in a simulation study, and are recommended for the future use.

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Moussa, M. A. A. "Clinical Trial Size: Fixed versus Group-Sequential Designs." Methods of Information in Medicine 25, no. 04 (1986): 233–36. http://dx.doi.org/10.1055/s-0038-1635470.

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SummaryThis paper deals with the estimation of the size of clinical trials for comparing two binomial proportions in both fixed and group-sequential designs. In the fixed size approach, it focuses on 1.) equal sample size design; 2.) unequal allocation designs that either maximize the test power subject to fixed total cost or minimize the total expenditure subject to prespecified power using the simplex procedure for function minimization. Emphasis is also placed on group-sequential designs, based upon closed stopping rules, multiple testing and range of clinical equivalence. The efficiency of different allocation designs is assessed by computing the power of the exact conditional Fisher-Irwin test. Designs were applied in planning a clinical trial in which the drug Pancuronium Bromide was compared with a standard treatment to reduce intraventricular haemorrhage in preterm infants.

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ŠAPOKA, Virginijus, Vytautas KASIULEVIČIUS, and Janina DIDŽIAPETRIENĖ. "How should a clinician interpret results of randomized controlled trials?" Acta medica Lituanica 17, no. 1-2 (2010): 30–34. http://dx.doi.org/10.15388/amed.2010.21689.

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Randomized controlled trials (RCTs) and systematic reviews are the most reliable methods of determining the effects of treatment. The randomization procedure gives a randomized controlled trial its strength. Random allocation means that all participants have the same chance of being assigned to each of the study groups. The choice of which end point(s) to select is critical to any study design. Intention-to-treat is the preferred approach to the analysis of clinical trials. Sample size calculations and data analyses have an important impact on the planning, interpretation, and conclusions of randomized trials. In this article, we discuss the problematic areas that can affect the outcome of a trial, such as blinding, sample size calculation, randomization; concealment allocation; intention of treating the analysis; selection of end points; selection of traditional versus equivalence testing, early stopped trials, selective publications. Keywords: randomized controlled trials, sample size, outcomes, type of analyses

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Azari, Doni, and I. Ketut Sutrisna. "PENGARUH BELANJA MODALDAN DAU TERHADAP KEMANDIRIAN KEUANGAN DAERAH DAN PERTUMBUHAN EKONOMI KABUPATEN/KOTA DI PROVINSI BALI." E-Jurnal Ekonomi Pembangunan Universitas Udayana 11, no. 8 (2022): 2929. http://dx.doi.org/10.24843/eep.2022.v11.i08.p02.

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ABSTRACT  The research was conducted for the 2013-2018 period with a sample size of 8 districts and 1 city. The type of data used in this research is secondary data. Secondary data used obtained the website of Bali Provincial Statistics Agency. Research objectives to be achieved: 1) to analyze the effect of capital expenditure and general allocation funds on regional financial independence in districts in Bali Province. 2) to analyze the effect of capital expenditure, general allocation funds, and regional financial independence on economic growth in districts in Bali Province. 3) to analyze the effect of capital expenditure and general allocation funds through regional financial independence on economic growth in districts in Bali Province. Analysis technique used is path analysis and the results of research on capital expenditures and general allocation funds have a significant positive effect on regional financial independence in districts in Bali Province. Capital expenditures and general allocations do not have a direct effect on economic growth, while regional financial independence has a significant positive effect on economic growth in districts in Bali Province. Capital expenditures and general allocation funds have an indirect effect on economic growth through regional financial independence in districts / cities in Bali Province. Keywords: Capital expenditures, general allocation funds, regional financial independence, economic growth.

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Maulana, Fajar, and Wida Fadhlia. "PENGARUH PENDAPATAN ASLI DAERAH, DANA ALOKASI KHUSUS, DAN LUAS WILAYAH TERHADAP BELANJA MODAL PADA PEMERINTAH KABUPATEN/KOTA DI PROVINSI ACEH." Jurnal Ilmiah Mahasiswa Ekonomi Akuntansi 5, no. 3 (2020): 362–71. http://dx.doi.org/10.24815/jimeka.v5i3.15390.

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This study aims to examine the effect of Local Revenue, Special Allocation Funds, and Area Size on Capital Expenditures. The data used in this study are secondary data obtained from the Aceh Financial Management Agency. The unit of analysis in this study is the 2014-2018 Regency/City Budget Realization Report. The sample in this study was Aceh Province consisting of 23 Regencies/Cities. The analysis technique used is multiple linear regression using SPSS 24. This study uses the independent variables Regional Original Revenue, Special Allocation Funds, and Area and the dependent variable Capital Expenditure. The results of this study indicate that in the simultaneous testing (F test), the variable Local Revenue, Special Allocation Funds, and Area Size have a significant effect on capital expenditure. . In the partial test, the variable of Original Regional Revenue, Special Allocation Funds, and area size affect capital expenditure.

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Gu, Rong, Han Li, Haipeng Dai, et al. "ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-Oriented Sample Size Allocation and Data Generation." Proceedings of the VLDB Endowment 16, no. 13 (2023): 4216–29. http://dx.doi.org/10.14778/3625054.3625059.

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Approximate query processing (AQP) is one of the key techniques to cope with big data querying problem on account that it obtains approximate answers efficiently. To address non-trivial sample selection and heavy sampling cost issues in AQP, we propose ShadowAQP, an efficient and accurate approach based on attribute-oriented sample size allocation and data generation. We select samples according to group-by and join attributes, and determine the sample size for each group of unique value combinations to improve query accuracy. We design a conditional variational autoencoder model with automatic table data encoding and model update strategies. To further improve accuracy and efficiency, we propose a set of extensions, including parallel multi-round sampling aggregation, data outlier-aware sampling, and dimension reduction optimization. Evaluation results on diversified datasets show that, compared with SOTA approaches, ShadowAQP achieves 5.8× query speed performance improvement on average (up to 12.8×), while reducing query error by 74% on average (up to 95%) at the same time.

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Balkir, Ibrahim, Fatma Hassan Ismail, and Sabri Al‐Segini. "Determinants of Fund Allocations Out of Housing Programs in the UAE." Journal of Economic and Administrative Sciences 22, no. 1 (2006): 25–40. http://dx.doi.org/10.1108/10264116200600002.

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The government of UAE helps its nationals to own their private houses. One of the programs that are designed to achieve this goal is the Sheikh Zayed Housing Program (SZHP). It pays grants and loans to nationals to have their own houses. This paper examines the factors that affect the fund allocation of SZHP. The researchers obtained the needed data from the offices of the SZHP. The sample consisted of 394 applicants for the year 2000. Regression models were used to assess the effect of family size, income, marital status and location of the applicants on fund allocation. The research findings show that income, family size and location have the most significant effect on fund allocation.

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Copas, Andrew J., and Richard Hooper. "Cluster randomised trials with different numbers of measurements at baseline and endline: Sample size and optimal allocation." Clinical Trials 17, no. 1 (2019): 69–76. http://dx.doi.org/10.1177/1740774519873888.

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Background/Aims: Published methods for sample size calculation for cluster randomised trials with baseline data are inflexible and primarily assume an equal amount of data collected at baseline and endline, that is, before and after the intervention has been implemented in some clusters. We extend these methods to any amount of baseline and endline data. We explain how to explore sample size for a trial if some baseline data from the trial clusters have already been collected as part of a separate study. Where such data aren’t available, we show how to choose the proportion of data collection devoted to the baseline within the trial, when a particular cluster size or range of cluster sizes is proposed. Methods: We provide a design effect given the cluster size and correlation parameters, assuming different participants are assessed at baseline and endline in the same clusters. We show how to produce plots to identify the impact of varying the amount of baseline data accounting for the inevitable uncertainty in the cluster autocorrelation. We illustrate the methodology using an example trial. Results: Baseline data provide more power, or allow a greater reduction in trial size, with greater values of the cluster size, intracluster correlation and cluster autocorrelation. Conclusion: Investigators should think carefully before collecting baseline data in a cluster randomised trial if this is at the expense of endline data. In some scenarios, this will increase the sample size required to achieve given power and precision.

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Wen, S. H., and C. K. Hsiao. "A grid-search algorithm for optimal allocation of sample size in two-stage association studies." Journal of Human Genetics 52, no. 8 (2007): 650–58. http://dx.doi.org/10.1007/s10038-007-0159-9.

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Ng, H. K. T., N. Balakrishnan, and P. S. Chan. "Optimal sample size allocation for tests with multiple levels of stress with extreme value regression." Naval Research Logistics 54, no. 3 (2007): 237–49. http://dx.doi.org/10.1002/nav.20207.

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Carpenter, Chris. "Geochemical Approaches Assist Production Allocation of Commingled Fluids." Journal of Petroleum Technology 75, no. 09 (2023): 90–93. http://dx.doi.org/10.2118/0923-0090-jpt.

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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 205130, “The Application of Petroleum Geochemical Methods to Production Allocation of Commingled Fluids,” by Richard Patience, Mark Bastow, and Martin Fowler, Applied Petroleum Technology, et al. The paper has not been peer reviewed. _ Geochemical-based methods for production monitoring and allocation are much lower-cost than use of production logging tools because no additional rig time or extra personnel are required at the well site. Additionally, no intervention to the production of hydrocarbons from a well is required, reducing operational risk. The complete paper summarizes these approaches and provides examples and describes a best practice that avoids a one-size-fits-all approach. Introduction Production allocation from petroleum geochemistry is defined here as the quantitative determination of the amount or portion of a commingled fluid to be assigned to two or more individual fluid sources at a particular moment in time based on fluid chemistry. It requires knowledge of the original chemical compositions of each of the fluids before mixing (referred to here as the “end members”) and the ability to identify statistically valid differences in their chemistries. The fluid may be oil, gas, or water. The concept is illustrated in Fig. 1a by mixing colors to represent the end members and commingled fluid. Production monitoring, in contrast, is the analysis of a time series of production fluids from the same source. This is demonstrated in Fig. 1b, where a change in chemistry can be observed between Days 60 and 100. Two data points at the same time are duplicates, proving that the change in chemistry is real and not an artifact. It is generally qualitative to semiquantitative in comparison with production allocation. Production allocation, therefore, is inherently more complex, in terms of both the sample requirements and data treatment. Allocation is the main focus of this paper. Allocation Sampling Strategies For a production allocation project, the three following distinct stages require samples: Stage 1: Can Contributing End Members Be Distinguished? In the initial stage, a determination must be made regarding whether end member reservoirs contributing to production can be distinguished based on their chemical composition. End Members From Conventional Plays. These typically are flowed samples from drillstem tests, bottomhole samples from wireline tools, or production tests, usually taken during the drilling of exploration or appraisal wells. Several issues must be considered when assessing the usefulness of such samples: - Are the end member samples representative of the reservoir as a whole? - Is the end member sample contaminated with organic compounds from drilling fluid? - When were the end members taken, how were they stored, and has there been significant alteration of the composition of the sample as a result?

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Sun, Tianye, Wei Sun, Changhao Sun, and Ruofei He. "Multi-UAV Formation Path Planning Based on Compensation Look-Ahead Algorithm." Drones 8, no. 6 (2024): 251. http://dx.doi.org/10.3390/drones8060251.

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This study primarily studies the shortest-path planning problem for unmanned aerial vehicle (UAV) formations under uncertain target sequences. In order to enhance the efficiency of collaborative search in drone clusters, a compensation look-ahead algorithm based on optimizing the four-point heading angles is proposed. Building upon the receding-horizon algorithm, this method introduces the heading angles of adjacent points to approximately compensate and decouple the triangular equations of the optimal trajectory, and a general formula for calculating the heading angles is proposed. The simulation data indicate that the model using the compensatory look forward algorithm exhibits a maximum improvement of 12.9% compared to other algorithms. Furthermore, to solve the computational complexity and sample size requirements for optimal solutions in the Dubins multiple traveling salesman model, a path-planning model for multiple UAV formations is introduced based on the Euclidean traveling salesman problem (ETSP) pre-allocation. By pre-allocating sub-goals, the model reduces the computational scale of individual samples while maintaining a constant sample size. The simulation results show an 8.4% and 17.5% improvement in sparse regions for the proposed Euclidean Dubins traveling salesman problem (EDTSP) model for takeoff from different points.

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Gulyaev, Pavel, Tibor Krenicky, Evgeny Shelkovnikov, and Aleksandr Korshunov. "Particle and Particle Agglomerate Size Monitoring by Scanning Probe Microscope." Applied Sciences 12, no. 4 (2022): 2183. http://dx.doi.org/10.3390/app12042183.

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In the present study, the use of a scanning probe microscope is described for monitoring the sizes of nanoparticles. Monitoring is the process of acquiring and analysing the set of overlapping images. The main analysis steps are image segmentation, determination of nanoparticles allocation and their sizes, determination of the overlap of images with one another, and exclusion of repeating measurements for the formation of the correct particle-size sampling. The thorough examination of commercial scanning probe microscopes, software, and image processing libraries showed that their capabilities are limited for image segmentation and determination of sizes in complex structured images. A method based on the surface curvature computation is proposed for the image segmentation (allocation of particles) and determination of particle sizes. The curvature is estimated using the surface area approximation with respect to the circumference. It is proposed to use sample displacement sensors as an aid for image stitching.

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Andhini, Safira, Juniati Gunawan, and Hung-Yi Chen. "INFLUENCE OF GENERAL ALLOCATION FUND, SPECIAL ALLOCATION FUND, AND CAPITAL EXPENDITURE ON FINANCIAL PERFORMANCE OF LOCAL GOVERNMENT." International Journal of Contemporary Accounting 5, no. 2 (2023): 125–40. http://dx.doi.org/10.25105/ijca.v5i2.17381.

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The goal of the research is to ascertain how capital expenditures, special allocation funds, and general allocation funds affect government financial performance as evaluated by regional financial independence. Through capital expenditure, special allocation funds, and general allocation fund, local governments can build infrastructure that will provide long-term benefits, provide services to the public, and develop regional investments. The Minister of Finance of the Republic of Indonesia in 2021 highlighted the very low realization of the regional government budget reports. The data used is the Financial Reports of the Regional Government, namely the budget as well as realization report, transfer to the region and village fund report, and regional government budget reports from Regencies and Cities of North Sumatra Province for 2017-2022. The population is 33 regencies aswell as cities in North Sumatra, with a total sample size of 198. Panel data regression using saturated sample approaches is the analytical approach used. Measurements for capital expenditures, special allocation funds, as well as variable general allocation funds are made using realized values represented in rupiah. This research also provides an international perspective to show that general allocation funds and capital expenditure have a significant positive influence on local government financial performance, and special allocation funds have a significant negative influence on local government financial performance.

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Forrest, Jessica, and James D. Thomson. "Pollen limitation and cleistogamy in subalpine Viola praemorsa." Botany 86, no. 5 (2008): 511–19. http://dx.doi.org/10.1139/b08-020.

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Early-flowering species may be especially susceptible to occasional pollen limitation and, therefore, may benefit from a mixed-mating strategy that provides reproductive assurance. We studied cleistogamous (CL) and chasmogamous (CH) fruit set of spring-flowering Viola praemorsa Dougl. ex Lindl. along an elevational gradient in the Rocky Mountains, testing whether pollen limitation or allocation to CL reproduction covaried with timing of flowering onset, within and across sites. Contrary to predictions, we found no pollen limitation of reproduction at any site, and variation among sites in the pattern of allocation to cleistogamy was not related to growing season length. Differences in reproductive strategy between early- and late-flowering plants within sites were attributable to differences in plant size, with relative allocation to cleistogamy increasing with size. This pattern has been found in some other cleistogamous species, and may indicate a cost of large CH floral displays, perhaps associated with geitonogamy or herbivory. We found no experimental evidence for resource reallocation in response to CH reproductive output, although a weak negative relationship between CH and CL fruit set across a larger sample of unmanipulated plants suggests such a trade-off. The significance of cleistogamy may be clarified by studying how pollinator visitation, self-pollination, and herbivore damage vary temporally and with floral display size.

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Journal articles on the topic 'Sample size allocation'

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