Small-area estimation with spatial similarity

A class of composite estimators of small area quantities that exploit spatial (distancerelated)similarity is derived. It is based on a distribution-free model for the areas, but theestimators are aimed to have optimal design-based properties. Composition is applied alsoto estimate some of the global parameters on which the small area estimators depend.It is shown that the commonly adopted assumption of random effects is not necessaryfor exploiting the similarity of the districts (borrowing strength across the districts). Themethods are applied in the estimation of the mean household sizes and the proportions ofsingle-member households in the counties (comarcas) of Catalonia. The simplest version ofthe estimators is more efficient than the established alternatives, even though the extentof spatial similarity is quite modest.

Optimal information transmission in organizations: Search and congestion

We propose a stylized model of a problem-solving organization whoseinternal communication structure is given by a fixed network. Problemsarrive randomly anywhere in this network and must find their way to theirrespective specialized solvers by relying on local information alone.The organization handles multiple problems simultaneously. For this reason,the process may be subject to congestion. We provide a characterization ofthe threshold of collapse of the network and of the stock of foatingproblems (or average delay) that prevails below that threshold. We buildupon this characterization to address a design problem: the determinationof what kind of network architecture optimizes performance for any givenproblem arrival rate. We conclude that, for low arrival rates, the optimalnetwork is very polarized (i.e. star-like or centralized ), whereas it islargely homogenous (or decentralized ) for high arrival rates. We also showthat, if an auxiliary assumption holds, the transition between these twoopposite structures is sharp and they are the only ones to ever qualify asoptimal.

Firm investment in imperfect capital markets: A structural estimation

We set up a dynamic model of firm investment in which liquidity constraintsenter explicity into the firm's maximization problem. The optimal policyrules are incorporated into a maximum likelihood procedure which estimatesthe structural parameters of the model. Investment is positively related tothe firm's internal financial position when the firm is relatively poor. This relationship disappears for wealthy firms, which can reach theirdesired level of investment. Borrowing is an increasing function of financial position for poor firms. This relationship is reversed as a firm's financial position improves, and large firms hold little debt.Liquidity constrained firms may be unused credits lines and the capacity toinvest further if they desire. However the fear that liquidity constraintswill become binding in the future induces them to invest only when internalresources increase.We estimate the structural parameters of the model and use them to quantifythe importance of liquidity constraints on firms' investment. We find thatliquidity constraints matter significantly for the investment decisions of firms. If firms can finance investment by issuing fresh equity, rather than with internal funds or debt, average capital stock is almost 35% higher overa period of 20 years. Transitory shocks to internal funds have a sustained effect on the capital stock. This effect lasts for several periods and ismore persistent for small firms than for large firms. A 10% negative shock to firm fundamentals reduces the capital stock of firms which face liquidityconstraints by almost 8% over a period as opposed to only 3.5% for firms which do not face these constraints.

The economics of organized crime and optimal law enforcement

This paper extends the optimal law enforcement literature to organized crime.We model the criminal organization as a vertical structure where the principal extracts some rents from the agents through extortion. Depending on the principal's information set, threats may or may not be credible. As long as threats are credible, the principal is able to fully extract rents.In that case, the results obtained by applying standard theory of optimal law enforcement are robust: we argue for a tougher policy. However, when threats are not credible, the principal is not able to fully extract rents and there is violence. Moreover, we show that it is not necessarily true that a tougher law enforcement policy should be chosen when in presence of organized crime.

Optimal law enforcement and criminal organization

In this paper, we take an organizational view of organized crime. In particular, we study the organizational consequences of product illegality attending at the following characteristics: (i) contracts are not enforceable in court, (ii) all participants are subject to the risk of being punished, (iii) employees present a major threat to the entrepreneur having the most detailed knowledge concerning participation, (iv) separation between ownership and management is difficult because record-keeping and auditing augments criminal evidence.

A finite-population revenue management model and a risk-ratio procedure for the joint estimation of population size and parameters

Many dynamic revenue management models divide the sale period into a finite number of periods T and assume, invoking a fine-enough grid of time, that each period sees at most one booking request. These Poisson-type assumptions restrict the variability of the demand in the model, but researchers and practitioners were willing to overlook this for the benefit of tractability of the models. In this paper, we criticize this model from another angle. Estimating the discrete finite-period model poses problems of indeterminacy and non-robustness: Arbitrarily fixing T leads to arbitrary control values and on the other hand estimating T from data adds an additional layer of indeterminacy. To counter this, we first propose an alternate finite-population model that avoids this problem of fixing T and allows a wider range of demand distributions, while retaining the useful marginal-value properties of the finite-period model. The finite-population model still requires jointly estimating market size and the parameters of the customer purchase model without observing no-purchases. Estimation of market-size when no-purchases are unobservable has rarely been attempted in the marketing or revenue management literature. Indeed, we point out that it is akin to the classical statistical problem of estimating the parameters of a binomial distribution with unknown population size and success probability, and hence likely to be challenging. However, when the purchase probabilities are given by a functional form such as a multinomial-logit model, we propose an estimation heuristic that exploits the specification of the functional form, the variety of the offer sets in a typical RM setting, and qualitative knowledge of arrival rates. Finally we perform simulations to show that the estimator is very promising in obtaining unbiased estimates of population size and the model parameters.

Heterogeneity-promoting optimal procurement

When procurement takes place in the presence of horizontally differentiated contractors, the design of the object being procured affects the resulting degree of competition. This paper highlights the interaction between theoptimal procurement mechanism and the design choice. Contrary to conventional wisdom, the sponsor's design choice, instead of homogenizingthe market to generate competition, promotes heterogeneity.

Dynamic optimal law enforcement with learning

We incorporate the process of enforcement learning by assuming that the agency's current marginal cost is a decreasing function of its past experience of detecting and convicting. The agency accumulates data and information (on criminals, on opportunities of crime) enhancing the ability to apprehend in the future at a lower marginal cost.We focus on the impact of enforcement learning on optimal stationary compliance rules. In particular, we show that the optimal stationary fine could be less-than-maximal and the optimal stationary probability of detection could be higher-than-otherwise.

Inequalities for a new data-based method for selecting nonparametric density estimates

We continue the development of a method for the selection of a bandwidth or a number of design parameters in density estimation. We provideexplicit non-asymptotic density-free inequalities that relate the $L_1$ error of the selected estimate with that of the best possible estimate,and study in particular the connection between the richness of the classof density estimates and the performance bound. For example, our methodallows one to pick the bandwidth and kernel order in the kernel estimatesimultaneously and still assure that for {\it all densities}, the $L_1$error of the corresponding kernel estimate is not larger than aboutthree times the error of the estimate with the optimal smoothing factor and kernel plus a constant times $\sqrt{\log n/n}$, where $n$ is the sample size, and the constant only depends on the complexity of the family of kernels used in the estimate. Further applications include multivariate kernel estimates, transformed kernel estimates, and variablekernel estimates.

Optimal law enforcement under asymmetric information

In this paper, we focus on the problem created by asymmetric informationabout the enforcer's (agent's) costs associated to enforcement expenditure. This adverse selection problem affects optimal law enforcement because a low cost enforcer may conceal its information by imitating a high cost enforcer, and must then be given a compensation to be induced to reveal its true costs. The government faces a trade-off between minimizing the enforcer's compensation and maximizing the net surplus of harmful acts. As a consequence, the probability of apprehension and punishment is usually reduced leading to more offenses being committed. We show that asymmetry of information does not affect law enforcement as long as raising public funds is costless. The consideration of costly raising of public funds permits to establish the positive correlation between asymmetry of information between government and enforcers andthe crime rate.

Biotic potential and reproductive parameters of Spodoptera eridania (Stoll) (Lepidoptera, Noctuidae) in the laboratory

Biotic potential and reprodutcive parameters of Spodoptera eridania (Stoll) (Lepidoptera, Noctuidae) in the laboratory: This study aimed to evaluate the biotic potential and reproductive parameters of Spodoptera eridania (Stoll, 1782) under controlled conditions (25 ± 1ºC, 70 ± 10% RH and 14 hour photophase). The longevity, pre-, post- and oviposition periods, fecundity and fertility of 15 couples was evaluated. The longevity of females (10.80 days) was not significantly higher than those of males (9.27 days). The mean durations of the pre, post and oviposition periods were 2.067, 0.600 and 8.133 days, respectively. The mean fecundity per female was 1,398 eggs and the mean fertility was 1,367.50 larvae. On average, females copulated 1.133 times. A strong positive correlation was observed between the number of mating and fecundity (r = 0.881, P <0.001). However a strong negative correlation was observed between the number of copulations and the duration of the pre-oviposition period (r = -0.826, P = 0.002) and longevity (r = -0.823, P = 0.001). The biotic potential of S. eridania was estimated at 1.894 x 10(25) individuals/female/year. The net reproductive rate (Ro) was 560.531 times per generation and the mean generation time (T) was 35.807 days. The intrinsic rate of increase (rm) was 0.177, with a finite rate of increase (l) of 1.193, per week

Use of 2D Sensitivity Encoding for Slow-Infusion Contrast-Enhanced Isotropic 3-T Whole-Heart Coronary MR Angiography.

OBJECTIVE. The purpose of this study was to improve the blood-pool signal-to-noise ratio (SNR) and blood-myocardium contrast-to-noise ratio (CNR) of slow-infusion 3-T whole-heart coronary MR angiography (MRA).SUBJECTS AND METHODS. In 2D sensitivity encoding (SENSE), the number of acquired k-space lines is reduced, allowing less radiofrequency excitation per cardiac cycle and a longer TR. The former can be exploited for signal enhancement with a higher radiofrequency excitation angle, and the latter leads to noise reduction due to lower data-sampling bandwidth. Both effects contribute to SNR gain in coronary MRA when spatial and temporal resolution and acquisition time remain identical. Numeric simulation was performed to select the optimal 2D SENSE pulse sequence parameters and predict the SNR gain. Eleven patients underwent conventional unenhanced and the proposed 2D SENSE contrast-enhanced coronary MRA acquisition. Blood-pool SNR, blood-myocardium CNR, visible vessel length, vessel sharpness, and number of side branches were evaluated.RESULTS. Consistent with the numeric simulation, using 2D SENSE in contrast-enhanced coronary MRA resulted in significant improvement in aortic blood-pool SNR (unenhanced vs contrast-enhanced, 37.5 +/- 14.7 vs 121.3 +/- 44.0; p < 0.05) and CNR (14.4 +/- 6.9 vs 101.5 +/- 40.8; p < 0.05) in the patient sample. A longer length of left anterior descending coronary artery was visualized, but vessel sharpness, coronary artery coverage, and image quality score were not improved with the proposed approach.CONCLUSION. In combination with contrast administration, 2D SENSE was found effective in improving SNR and CNR in 3-T whole-heart coronary MRA. Further investigation of cardiac motion compensation is necessary to exploit the SNR and CNR advantages and to achieve submillimeter spatial resolution.

Identification of optimal structural connectivity using functional connectivity and neural modeling.

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.