Multinomial Selection Research Articles

Determinants of household's waste disposal practices and willingness to participate in reducing the flow of plastics into the ocean: Evidence from coastal city of Lagos Nigeria.

Marine plastic pollution is a critical environmental challenge facing policymakers globally. To reduce marine plastic pollution by engaging the people, this study estimated the determinants of waste disposal approach by households, their willingness to participate in road gutters/drainage channels cleanup program and the number of man-days they are willing to contribute. The study used a total of 600 households drawn from 30 enumeration areas. A semi-structured questionnaire was employed in data collection. Means, percentages, multinomial logit model and Heckman selection model were employed in data analysis. The study found that most (67.42 percent) of the households in the coastal city of Lagos engage in illegal waste disposal. Some variables, household size, involvement in previous community cleanup activities, receipt of waste management information, payment of waste management fee, and having a dumpster in a locality, significantly reduce the likelihood of illegal waste disposal. The study also found that most (75.50 percent) of the households were willing to clean up road gutters/drainage channels; however, most (83.20 percent) were only willing to contribute one man-day (eight hours) in a week. Gender and previous participation in voluntary service significantly influenced both households’ willingness to participate and the number of the man-days they are willing to contribute. Women are more likely to participate and contribute man-days to the activity. Education, household size and amount paid as waste management fee significantly reduced the number of man-days households are willing to contribute. In contrast, the provision of information on waste management significantly increased the number of days they are likely to participate. The study recommended providing waste management information and dumpsters to reduce illegal waste disposal, mobilizing citizens, especially women, the less educated and low waste fee-paying households, through well-packaged information about plastic pollution.

Processing technologies for undervalued grains in rural India: on target to help the poor?

Finger millet (ragi) is increasingly recognized as a nutritious staple by Indian consumers and policy makers. Though previously regarded as a poor person’s crop, the benefits of enhanced ragi consumption may bypass the poor. Because home processing is arduous, small flour mills have been introduced to help. With geo-referenced survey data from a pilot area in the Kolli Hills region of Tamil Nadu, India, we examined determinants of mill use and use intensity employing a two stage multinomial selection model. Overall, we found that the mill technology was not pro-poor, in that poor people do not tend to use the mills more than wealthier people, or use them at higher rates. We identified the location of mills as being a key factor in preventing more use of mills by the poor. Therefore, to better serve the poor, external agencies would have to deliberately locate mills in poor communities. For this to be feasible, changes to make this technology work better with poor communities may be required, such as the use of less capital intensive technology such as hand- or pedal-power, rather than reliance on electrical power.

Labor migration and impact of remittances on poverty and income inequality: evidence from estate sector of Sri Lanka

There is a continuing debate over the impacts of migration on the developing nations despite the ever-increasing size of internal and international remittances. Moreover, a little attention has been paid to analyze the impact of these financial transfers on poverty and inequality of those countries. This study, using a nationally representative sample, assessed the impacts of migration and remittances on poverty and inequality of estate sector households of Sri Lanka. A multinomial logit-ordinary least squares two-stage selection control model and a simulation analysis were used to estimate the impact of migration and remittances on poverty and inequality. Results reveal that internal and international remittances reduce poverty incidence by 2.14% and 2.32%, depth of poverty by 1.33% and 0.98%, and severity of poverty by 0.63% and 0.48%, respectively. Results further suggest that income inequality slightly decreased due to internal and international remittances. Moreover, the findings support a growing view in the literature that migration is a livelihood strategy and it helps in alleviating poverty.

Constrained vs unconstrained labor supply: the economics of dual job holding

This paper develops a unified model of dual and unitary job holding based on a Stone-Geary utility function. The model incorporates both constrained and unconstrained labor supply. Panel data methods are adapted to accommodate unobserved heterogeneity and multinomial selection into six mutually exclusive labor supply regimes. We estimate the wage and income elasticities arising from selection and unobserved heterogeneity as well as from the Stone-Geary Slutsky equations. The labor supply model is estimated with data from the British Household Panel Survey 1991–2008. Among dual job holders, our study finds that the Stone-Geary income and wage elasticities are much larger for labor supply to the second job compared with the main job. When the effects of selection and unobserved heterogeneity are taken account of, the magnitudes of these elasticities on the second job tend to be significantly reduced.

Optimal solution to the multinomial selection problem for two alternatives

ABSTRACTThe multinomial selection problem is to find a stopping policy for repeated independent trials, each of which reports a winner among competing alternatives that has low expected cost and high probability of correct selection (PCS) of the best alternative. In 1959, Bechhofer, Elmaghraby, and Morse formulated the problem as minimizing the worst-case expected number of trials, subject to a lower bound on PCS and upper bound on the maximum number of trials, over all probability vectors outside an indifference zone. For the case of two alternatives, we prove that if one employs a particular probability vector known as the slippage configuration, then a linear program always finds an optimal stopping policy.

Lower bounds for expected sample size of sequential procedures for the multinomial selection problems

ABSTRACTIn this article, lower bounds for expected sample size of sequential selection procedures are constructed for the problem of selecting the most probable event of k-variate multinomial distribution. The study is based on Volodin’s universal lower bounds for expected sample size of statistical inference procedures. The obtained lower bounds are used to estimate the efficiency of some selection procedures in terms of their expected sample sizes.

Multinomial Selection in the Presence of Infinite Alternatives

We propose a new procedure for the multinomial selection problem to solve a real problem of any modern Air Force: the elaboration of better air-to-air tactics for Beyond Visual Range air-to-air combat that maximize its aircraft survival probability H(θ, ω), as well as enemy aircraft downing probability G(θ, ω). In this study, using a low-resolution simulator with generic parameters for the aircraft and missiles, we could increase an average success rate of 16.69% and 16.23% for H(θ, ω) and G(θ, ω), respectively, to an average success rate of 76.85% and 79.30%. We can assure with low probability of being wrong that the selected tactic has greater probability of yielding greater success rates in both H(θ, ω) and G(θ, ω) than any simulated tactic.

From public to mixed ownership in local public services provision: an empirical analysis

ABSTRACTThere is a long tradition of companies that provide local public services, such as energy distribution or solid waste collection, being strategically important for the promotion of the development of areas where they are present and having a great influence on citizens’ quality of life. This article examines the determinants of the choice of ownership structure for Italian companies that provide local public services, and the effects that this choice has on their performance. Economic and financial data at company level was merged with economic, political, financial and territorial data on the municipality with the majority of shares in it. A two-stage multinomial selection model was employed, in order to carry out the analysis with more than two alternative ownership structures and to control for endogeneity. The empirical evidence indicates that the municipality’s political and budgetary conditions matter in the choice of ownership structure. Although the computed Average Treatment Effects seem to indicate that mixed ownership increases operating efficiency, the profitability indicators provide evidence to the contrary.

Determinants of Earnings and Selection into Labor Market by Controlling Selection Bias in Bangladesh

This paper uses the data of Household Income and Expenditure Survey (HIES) 2005 to estimate the determinants of earnings and selection into labor market in Bangladesh. We deal with the selection bias in earnings by using a maximum likelihood system of equations, and a multinomial selection approach is used modeling for selection into the labor market. By instrumenting years of schooling in both the multinomial selection approach and the earnings equations, we deal with reverse causality between educational attainment and earnings. We find that the estimated parameters of the earnings equation under multinomial selection approach differs from ordinary least square (OLS) estimates and a binomial selection procedure. The estimated parameters that vary the most with those related variables have the strongest impact on multinomial selection into the different labor-market statuses. We also find that workers with higher educational attainment are more likely to search out a salaried employees’ job, which non-salaried work is as another to inactivity. DOI: http://dx.doi.org/10.3329/dujs.v62i1.21953 Dhaka Univ. J. Sci. 62(1): 11-16, 2014 (January)

Optimal Selection of the Most Probable Multinomial Alternative

Multinomial selection is concerned with selecting the most probable (best) multinomial alternative. The alternatives compete in a number of independent trials. In each trial, each alternative wins with an unknown probability specific to that alternative. A long-standing research goal has been to find a procedure that minimizes the expected number of trials subject to a lower bound on the probability of correct selection ((CS)). Numerous procedures have been proposed over the past 55 years, all of them suboptimal, for the version where the number of trials is bounded. We achieve the goal in the following sense: For a given multinomial probability vector, lower bound on (CS), and upper bound on trials, we use linear programming (LP) to construct a procedure that is guaranteed to minimize the expected number of trials. This optimal procedure may necessarily be randomized. We also present a mixed-integer linear program (MIP) that produces an optimal deterministic procedure. In our computational studies, the MIP always outperforms previously existing methods from the literature, with a modest additional benefit arising from the LP's randomized procedure.

The Slippage Configuration Is Always the Least Favorable Configuration for Two Alternatives

Performance guarantees for multinomial selection procedures are usually derived by finding the least favorable configuration (LFC)—the one for which the probability of correct selection is minimum outside the indifference zone—and then evaluating the procedure on that configuration. The slippage configuration has been proved to be the LFC for several procedures and has been conjectured to be the worst for some other procedures. The principal result of this article unifies and extends all previous results for two alternatives: the slippage configuration is the worst for all procedures that have a finite expected number of trials and always select the alternative with more successes. A generalization of the key inequality in the proof to an arbitrary number of alternatives is conjectured.

A restricted multinomial hybrid selection procedure

Analysts using simulation models often must assess a large number of alternatives in order to determine which are most effective. If effectiveness corresponds to the likelihood of yielding the best outcome, this becomes a multinomial selection problem. Unfortunately, existing procedures were developed primarily for evaluating small sets of alternatives, so parameters required to implement them may not be readily available or the sampling costs may be prohibitive when a large number of alternatives are present. We propose a truncated, sequential multinomial subset selection procedure that restricts the maximum subset size. Numerical comparisons show that our procedure can be much more efficient than the leading unrestricted procedure. Our procedure requires only one calculated parameter rather than four. We provide extensive tables for cases involving large numbers of alternatives.

Average worth estimation of the selected subset of Poisson populations

Let Π1, …, Π p be p(p≥2) independent Poisson populations with unknown parameters θ1, …, θ p , respectively. Let X i denote an observation from the population Π i , 1≤i≤p. Suppose a subset of random size, which includes the best population corresponding to the largest (smallest) θ i , is selected using Gupta and Huang [On subset selection procedures for Poisson populations and some applications to the multinomial selection problems, in Applied Statistics, R.P. Gupta, ed., North-Holland, Amsterdam, 1975, pp. 97–109] and (Gupta et al. [On subset selection procedures for Poisson populations, Bull. Malaysian Math. Soc. 2 (1979), pp. 89–110]) selection rule. In this paper, the problem of estimating the average worth of the selected subset is considered under the squared error loss function. The natural estimator is shown to be biased and the UMVUE is obtained using Robbins [The UV method of estimation, in Statistical Decision Theory and Related Topics-IV, S.S. Gupta and J.O. Berger, eds., Springer, New York, vol. 1, 1988, pp. 265–270] UV method of estimation. The natural estimator is shown to be inadmissible, by constructing a class of dominating estimators. Using Monte Carlo simulations, the bias and risk of the natural, dominated and UMVU estimators are computed and compared.

Noise characteristics of heterodyne/homodyne frequency-domain measurements

We theoretically develop and experimentally validate the noise characteristics of heterodyne and/or homodyne measurements that are widely used in frequency-domain diffusive imaging. The mean and covariance of the modulated heterodyne output are derived by adapting the random amplification of a temporal point process. A multinomial selection rule is applied to the result of the temporal noise analysis to additionally model the spatial distribution of intensified photons measured by a charge-coupled device (CCD), which shows that the photon detection efficiency of CCD pixels plays an important role in the noise property of detected photons. The approach of using a multinomial probability law is validated from experimental results. Also, experimentally measured characteristics of means and variances of homodyne outputs are in agreement with the developed theory. The developed noise model can be applied to all photon amplification processes.

Multinomial Selection for Comparison with a Standard

The multinomial selection problem is considered under the formulation of comparison with a standard, where each system is required to be compared to a single system, referred to as a “standard,” as well as to other alternative systems. The goal is to identify systems that are better than the standard, or to retain the standard when it is equal to or better than the other alternatives in terms of the probability to generate the largest or smallest performance measure. We derive new multinomial selection procedures for comparison with a standard to be applied in different scenarios, including exact small-sample procedure and approximate large-sample procedure. Empirical results and the proof are presented to demonstrate the statistical validity of our procedures. The tables of the procedure parameters and the corresponding exact probability of correct selection are also provided.

Winter space use and social behaviors of Chinese Grouse (Bonasa sewerzowi) at Lianhuashan mountains, Gansu, China

Winter movement and social behavior of Chinese Grouse (Bonasa sewerzowi) were studied at Lianhuashan Natural Reserve, northwest China from 1995 to 2009. According to radio-tracking data from 25 male Chinese Grouse, their home ranges in winter ranged from 6.0 ± 1.6 ha (n = 9) to 16.0 ± 2.6 ha (n = 10). Most females and all the yearlings moved from breeding areas and formed mixed-sex flocks. However, males adopted one of three social behaviors and movement patterns, including: staying in the territory, moving to the winter flocking area, or wandering between the spring territory and the wintering area. In order to find out which factors influence male social behavior, we used multinomial logistic regression and AIC model selection to compare the habitat characteristics of male Chinese Grouse breeding territories. Modeling results indicated that food, cover, topography, and future breeding opportunity were the main variables determining social behavior in winter.

Multinomial Selection Problem: A Study of BEM and AVC Algorithms

The two well-known and widely used multinomial selection procedures Bechhofor, Elmaghraby, and Morse (BEM) and all vector comparison (AVC) are critically compared in applications related to simulation optimization problems. Two configurations of population probability distributions in which the best system has the greatest probability p i of yielding the largest value of the performance measure and has or does not have the largest expected performance measure were studied. The numbers achieved by our simulations clearly show that none of the studied procedures outperform the other in all situations. The user must take into consideration the complexity of the simulations and the performance measure probability distribution properties when deciding which procedure to employ. An important discovery was that the AVC does not work in populations in which the best system has the greatest probability p i of yielding the largest value of the performance measure but does not have the largest expected performance measure.

A Knowledge-Based Approach for Item Exposure Control in Computerized Adaptive Testing

The purpose of this study is to investigate a functional relation between item exposure parameters (IEPs) and item parameters (IPs) over parallel pools. This functional relation is approximated by a well-known tool in machine learning. Let P and Q be parallel item pools and suppose IEPs for P have been obtained via a Sympson and Hetter–type simulation. Based on these simulated parameters, a functional relation k = fP ( a, b, c) relating IPs to IEPs of P is obtained by an artificial neural network and used to estimate IEPs of Q without tedious simulation. Extensive experiments using real and synthetic pools showed that this approach worked pretty well for many variants of the Sympson and Hetter procedure. It worked excellently for the conditional Stocking and Lewis multinomial selection procedure and the Chen and Lei item exposure and test overlap control procedure. This study provides the first step in an alternative means to estimate IEPs without iterative simulation.

Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates

We propose Bayesian methods for estimating parameters in generalized linear models (GLMs) with nonignorably missing covariate data. We show that when improper uniform priors are used for the regression coefficients, phi, of the multinomial selection model for the missing data mechanism, the resulting joint posterior will always be improper if (i) all missing covariates are discrete and an intercept is included in the selection model for the missing data mechanism, or (ii) at least one of the covariates is continuous and unbounded. This impropriety will result regardless of whether proper or improper priors are specified for the regression parameters, beta, of the GLM or the parameters, alpha, of the covariate distribution. To overcome this problem, we propose a novel class of proper priors for the regression coefficients, phi, in the selection model for the missing data mechanism. These priors are robust and computationally attractive in the sense that inferences about beta are not sensitive to the choice of the hyperparameters of the prior for phi and they facilitate a Gibbs sampling scheme that leads to accelerated convergence. In addition, we extend the model assessment criterion of Chen, Dey, and Ibrahim (2004a, Biometrika 91, 45-63), called the weighted L measure, to GLMs and missing data problems as well as extend the deviance information criterion (DIC) of Spiegelhalter et al. (2002, Journal of the Royal Statistical Society B 64, 583-639) for assessing whether the missing data mechanism is ignorable or nonignorable. A novel Markov chain Monte Carlo sampling algorithm is also developed for carrying out posterior computation. Several simulations are given to investigate the performance of the proposed Bayesian criteria as well as the sensitivity of the prior specification. Real datasets from a melanoma cancer clinical trial and a liver cancer study are presented to further illustrate the proposed methods.

Do the Near-Elderly Value Mortality Risks Differently?

Wage hedonic models are estimated with the Health and Retirement Study to measure the risk-wage tradeoffs (value of statistical lives) for older workers. The analysis explicitly allows for multiple employment states, including retirement, using a multinomial selection model. The results suggest that the oldest and most risk-averse workers require significantly higher, not lower, compensation to accept increases in job-related fatality risks.