Largest Probability Of Success Research Articles

Can fossil fuel endowments steer economic development? Evidence from the linkages approach

The economic and political literature is not conclusive about whether large natural resource endowments diminish growth and development prospects, about the diverse political, economic, and institutional causal mechanisms that lead to Resource Curse and Dutch Disease phenomena, and about the most suitable and effective counteracting measures against these problems. This article adds further evidence to the debate through the linkages approach, primarily associated with Albert Hirschman. By calculating Rasmussen-Hirschman linkages for 42 countries from the WIOD database for 2000–2014, which are subdivided into two groups of countries - one with above and another with below OECD average fuel exports, it suggests that there is a larger likelihood that countries that export fewer fossil fuels to experience higher growth in their manufacturing sector linkages. However, because these two groups are not homogeneous in their relationship between GDP per Capita growth, the size of their linkages, and the growth of the size of their manufacturing value-added in their GDPs, a panel data econometric exercise further subdivides the two country groups in above and below-average GDP per capita. It regresses the manufacturing sector’s backward and linkages with selected variables. The econometric exercise presents evidence that countries with higher GDP per capita and that export more fuels than the OECD average have a larger probability of success than countries with above-average fossil fuels exports and below OECD average GDP per capita. This shows that natural resource exploitation and fuel exports are more likely to be a curse in countries that have not reached a certain threshold of institutional quality, physical and intangible capital accumulation, making it more difficult for them to reap the fruits of natural resource exploitation fully.

An Improvement of the Biased-PPSZ Algorithm for the 3SAT Problem

Hansen, Kaplan, Zamir and Zwick (STOC 2019) introduced a systematic way to use “bias” for predicting an assignment to a Boolean variable in the process of PPSZ and showed that their biased PPSZ algorithm achieves a relatively large success probability improvement of PPSZ for Unique 3SAT. We propose an additional way to use “bias” and show by numerical analysis that the improvement gets increased further.

Identifying Which of J Independent Binomial Distributions Has the Largest Probability of Success

Let p1,…, pJ denote the probability of a success for J independent random variables having a binomial distribution and let p(1) ≤ … ≤ p(J) denote these probabilities written in ascending order. The goal is to make a decision about which group has the largest probability of a success, p(J). Let p̂1,…, p̂J denote estimates of p1,…,pJ, respectively. The strategy is to test J − 1 hypotheses comparing the group with the largest estimate to each of the J − 1 remaining groups. For each of these J − 1 hypotheses that are rejected, decide that the group corresponding to the largest estimate has the larger probability of success. This approach has a power advantage over simply performing all pairwise comparisons. However, the more obvious methods for controlling the probability of one more Type I errors perform poorly for the situation at hand. A method for dealing with this is described and illustrated.

Selection among Bernoulli populations in comparison with a standard

In this article we consider the problem of comparing several Bernoulli populations in order to identify the population producing the largest success probability that is also larger than a given standard. We propose a fixed sample size procedure for which we develop the probability of a correct selection and the least favorable configuration. We also propose a curtailed version of the fixed sample size procedure and show that the curtailed procedure reaches the same probability of a correct selection as the fixed sample size procedure, while using fewer observations. We provide tables to implement the procedures and illustrate them via an example. We use simulations to estimate the savings by the curtailment procedure.

A Phase I/II trial design when response is unobserved in subjects with dose-limiting toxicity

We propose a Phase I/II trial design in which subjects with dose-limiting toxicity are not followed for response, leading to three possible outcomes for each subject: dose-limiting toxicity, absence of therapeutic response without dose-limiting toxicity, and presence of therapeutic response without dose-limiting toxicity. We define the latter outcome as a 'success,' and the goal of the trial is to identify the dose with the largest probability of success. This dose is commonly referred to as the most successful dose. We propose a design that accumulates information on subjects with regard to both dose-limiting toxicity and response conditional on no dose-limiting toxicity. Bayesian methods are used to update the estimates of dose-limiting toxicity and response probabilities when each subject is enrolled, and we use these methods to determine the dose level assigned to each subject. Due to the need to explore doses more fully, each subject is not necessarily assigned the current estimate of the most successful dose; our algorithm may instead assign a dose that is in a neighborhood of the current most successful dose. We examine the ability of our design to correctly identify the most successful dose in a variety of settings via simulation and compare the performance of our design to that of competing approaches.

A Bayesian Inference and Stochastic Dynamic Programming Approach to Determine the Best Binomial Distribution

In this research, we employ Bayesian inference and stochastic dynamic programming approaches to select the binomial population with the largest probability of success from n independent Bernoulli populations based upon the sample information. To do this, we first define a probability measure called belief for the event of selecting the best population. Second, we explain the way to model the selection problem using Bayesian inference. Third, we clarify the model by which we improve the beliefs and prove that it converges to select the best population. In this iterative approach, we update the beliefs by taking new observations on the populations under study. This is performed using Bayesian rule and prior beliefs. Fourth, we model the problem of making the decision in a predetermined number of decision stages using the stochastic dynamic programming approach. Finally, in order to understand and to evaluate the proposed methodology, we provide two numerical examples and a comparison study by simulation. The results of the comparison study show that the proposed method performs better than that of Levin and Robbins (1981) for some values of estimated probability of making a correct selection.

Analyzing Hybrid Randomized Response Data with a Binomial Selection Procedure

The operating characteristics (OCs) of an indifference-zone ranking and selection procedure are derived for randomized response binomial data. The OCs include tables and figures to facilitate tradeoffs between sample size and a stated probability of a correct selection, i.e., correctly identifying the binomial population (out of k ≥ 2) characterized by the largest probability of success. Measures of efficiency are provided to assist the analyst in selection of an appropriate randomized response design for the collection of the data. A hybrid randomized response model, which includes the Warner model and the Greenberg et al. model, is introduced to facilitate comparisons among a wider range of statistical designs than previously available. An example comparing failure rates of contraceptive methods is used to illustrate the use of these new results.

Multiparticle entanglement manipulation under positive partial transpose preserving operations

We consider the transformation of multipartite states in the single-copy setting under positive-partial-transpose-preserving operations (PPT operations) and obtain both qualitative and quantitative results. First, for some pure-state transformations that are impossible under local operations and classical communication (LOCC), we demonstrate that they become possible with a surprisingly large success probability under PPT operations. Furthermore, we clarify the convertibility of arbitrary multipartite pure states under PPT operations and show that a drastic simplification in the classification of pure-state entanglement occurs when the set of operations is switched from LOCC to PPT operations. Indeed, the infinitely many types of LOCC-incomparable entanglement are reduced to only one type under the action of PPT operations. This is a clear manifestation of the increased power afforded by the use of PPT-bound entanglement. In addition, we further enlarge the set of operations to clarify the effect of another type of bound entanglement, multipartite unlockable bound entanglement, and show that a further simplification occurs. As compared to pure states a more complicated situation emerges in mixed-state settings. While single-copy distillation becomes possible under PPT operations for some mixed states it remains impossible for other mixed states.

Comparações entre medidas de dissimilaridade e estatísticas multivariadas como critérios no direcionamento de hibridações em aveia

Medidas de dissimilaridade são artifícios que os melhoristas de plantas dispõem para identificarem genótipos distantes na obtenção de populações segregantes desejadas. Assim, o presente trabalho foi realizado para estabelecer relação entre as diferentes técnicas multivariadas utilizadas em estimativas de divergência genética. O experimento foi desenvolvido no ano agrícola de 2001, quando doze cultivares de aveia foram testados para sete caracteres agronômicos, utilizando-se o delineamento experimental de blocos casualizados com quatro repetições. As distâncias Euclidiana e de Mahalanobis evidenciaram baixa correlação (0,529) e, quando utilizadas na confecção de dendrogramas, não possibilitaram a formação de agrupamentos similares. As análises gráficas por componentes principais e variáveis canônicas também apresentaram dispersões distintas. Entretanto, apesar das discrepâncias observadas entre as metodologias analisadas, foi possível a identificação de genótipos dissimilares e com média elevada, que poderão ser utilizados com grande probabilidade de sucesso em hibridações dirigidas em aveia.

A random-size subset approach to the selection problem

The problem considered is to select the best population from among a finite number of populations. A new approach is proposed which is to select a random-size subset of populations that will contain the best population with a preassigned probability. This new approach is intuitively natural and needs no tables. For illustration, it is applied to four specific problems: the normal distribution with the largest mean, the normal distribution with the largest absolute value of mean, the Bernoulli distribution with the largest success probability, and the coin with the largest bias.

An improvement on paulson s sequential ranking procedure

Using Jensen s inequality we obtain a tighter bound on the probability of making a correct selection in Paulson s procedure for selecting the normal population which has the largest population mean by replacing Boole s inequality by a geometric inequality As a consequence we are able to use a sharper value for the constant aλ in Paulson s procedure If more than two populations are involved this should lead to an improvement in the expected number of stages to termination and expected total number of observations which is uniform in {delta;∗ P∗}. We also use the same method to improve Swanepoel and Geertsema's procedure for selecting the normal population which has the largest population mean Hoel and Mazumdar s extension of Paulson's procedure for a class of distributions in the Koopman-Darmois family and Hoel and Sobel s elimination play the winner procedure for selecting the binomial population with the largest probability of success

An optimal sequential procedure for selecting the best bernoulli process—a review

AbstractThis article describes a new closed adaptive sequential procedure proposed by Bechhofer and Kulkarni for selecting the Bernoulli population which has the largest success probability. It can be used effectively for selecting the production process with the largest proportion of conforming items, and thus is applicable in vendor selection situations. The performance of this procedure is compared to that of the Sobel‐Huyett single‐stage procedure, and to a curtailed version of the single‐stage procedure, all of which guarantee the same probability of a correct selection. Optimal properties of the Bechhofer‐Kulkarni procedure are stated; quantitative assessments of important performance characteristics of the procedure are given. These demonstrate conclusively the superiority of the new procedure over that of the competing procedures. Relevant areas of application (including clinical trials) are described. Appropriate literature references are provided.

Selecting the highest probability in binomial or multinomial trials

Some sequential procedures are considered for selecting the binomial population with largest success probability or for selecting the multinomial outcome with highest cell probability. Procedures with and without sequential elimination of inferior populations are evaluated with respect to the expected probability of the population selected.

A Two-Stage Procedure for Selecting the Best of Several Binomial Populations

In many situations in education and psychology it is desired to select from k binomial populations the one having the largest probability of success. For example, if a pass/fail type of test is administered at the end of an instructional program and if we have several methods of instruction from which to choose, it is desirable to choose the one which maximizes the probability of passing the test. This paper describes a two-stage procedure for accomplishing this goal. The solution is conservative in the sense that it is possible to determine the number of observations required so that the best population is chosen with probability at least P*, 2- k < P* < 1.

A methodology for identification, evaluation and comparison of R&D projects in R&D planning

Many models have been developed to aid research and development (R&D) planning in comparing, evaluating and selecting alternative projects at either micro or macro level [7] [8] [9]. Most of these models have been conceptual rather than being operational. Even the number of operational models decrease to a few as the level of R&D planning raises from the micro level to macro level, because of the existence of complex conflicting goals. For R&D planning at national level, socio-economic development goals require the R&D projects to simultaneously seek the highest economic profitibility, social utility, technological advance, scientific significance or the largest probability of success. However limited resources restrict the decision-maker to some maximum budget, a minimum number of projects to be worked on, and a minimum levels of accomplishment on specific areas. This is especially true for developing countries.In the present paper an operational methodology is being suggested for comparing, evaluating and selecting alternative R&D projects during the process of R&D planning in a developing country. An application of this methodology to the R&D planning for the Turkish Iron and Steel Industry (ISI) is presented as an example. A multiple criteria scoring model using a weighted sum of factor scores is developed to identify, compare and evaluate the alternative R&D projects which are supposed to solve the national socio-economic problems. A model for the selection of R&D projects is not suggested, because there are so many models for this purpose in the literature. An appropriate model should be selected from the model bank available in the literature.

A two-stage procedure for choosing the better of two binomial populations

SUMMARY In an earlier paper Sobel & Weiss (1970) examined two sampling schemes for choosing the binomial population with the larger probability of success. The two sequential allocation procedures were discussed in the light of their ability to reach a decision while using as small a number as possible of tests with the poorer population. It was shown that neither sampling scheme was uniformly better than the other with respect to that criterion. However, one sampling procedure, the Play-the-Winner rule (Robbins, 1956; Zelen, 1969), was superior, provided that the probability of success of the better population was greater than a number roughly equal to A. Here we investigate a two-stage procedure in which the first stage is used to estimate the probability of success of the better population. It is shown that a fairly small estimation stage, approximately five tests with each population, allows one to benefit considerably from the choice between the two sampling methods available in the second stage.

Play-the-winner sampling for selecting the better of two binomial populations

The sequential allocation of treatments by an experimenter is considered for determining which of two binomial populations has the larger probability of success. Of particular interest in this study is a 'Play-the-Winner' (PW) sampling rule which prescribes that one continues with the same population after each success and one switches to the opposite population after each failure. The performance of the PW rule is examined for the selection problem, i.e. for selecting the better population with probability P* when the two singletrial probabilities of success, p and p', differ by at least A*, where P* and i\* are prescribed. A comparison is made between PW sampling and 'Vector-at-a-Time' (VT) sampling. In comparing results a criterion used is the expected number of failures that could have been avoided by using the better population throughout. It is shown for a particular common termination rule that with A* close to zero the PW sampling is superior to VT sampling if and only if I (p +p') > J. Other comparisons are also discussed.