Input Aggregation in Models of Data Envelopment Analysis: A Statistical Test with an Application to Indian Manufacturing

A problem frequently encountered in Data Envelopment Analysis (DEA) is that the total number of inputs and outputs included tend to be too many relative to the sample size. One way to counter this problem is to combine several inputs (or outputs) into (meaningful) aggregate variables reducing thereby the dimension of the input (or output) vector. A direct effect of input aggregation is to reduce the number of constraints. This, in its turn, alters the optimal value of the objective function. In this paper, we show how a statistical test proposed by Banker (1993) may be applied to test the validity of a specific way of aggregating several inputs. An empirical application using data from Indian manufacturing for the year 2002-03 is included as an example of the proposed test.

Input Price Variation Across Locations and a Generalized Measure of Cost Efficiency

We propose a nonparametric model for global cost minimization as a framework for optimal allocation of a firm's output target across multiple locations, taking account of differences in input prices and technologies across locations. This should be useful for firms planning production sites within a country and for foreign direct investment decisions by multi-national firms. Two illustrative examples are included. The first example considers the production location decision of a manufacturing firm across a number of adjacent states of the US. In the other example, we consider the optimal allocation of US and Canadian automobile manufacturers across the two countries.

The astrological works of Abraham Ibn Ezra : a literary and linguistic study with special reference to the Old French translation of Hagin

by Raphael Levy

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Minimum water input event for seedling emergence of three native perennial grasses of the Central Monte desert (Argentina) influenced by the effect of shade and the season of the year

In deserts, seedling emergence occurs only after precipitation threshold has been exceeded, however, the presence of trees modifies microenvironmental conditions that might affect the effectiveness of a water pulse. In the Monte desert, Prosopis flexuosa trees generate different micro-environmental conditions that might influence grass seedlings establishment. The objective of this work was: a) to know the effective minimum water input event that triggers the emergence of native perennial grass seedlings; b) to relate this fact with the effect of the shade of P. flexuosa canopy and the seasonal temperatures. Three important forage species of the Monte were studied: Pappophorum caespitosum and Trichloris crinita, with C4, and Jarava ichu, with C3 metabolism. Each season, seeds of these species were sown in pots placed at two light conditions: shade (similar to P. flexuosa cover) and open area, and with seven irrigation treatments (0, 10, 20, 30, 40, 2*10 and 3*10 mm). J. ichu did not emerge in any of the treatments. Significant seedling emergence was registered for P. caespitosum and T. crinita in shade conditions with 40 mm irrigation treatment in summer. Since 40 mm precipitation events are infrequent in the Monte, seedling emergence for these species would be restricted to exceptional rainy years. The facilitating effect of P. flexuosa shade would be important during the hot season.

Análisis de la composición y dinámica de grupos de investigación en perspectiva comparada input-ouput : Estudio de caso en Bibliotecología y Ciencia de la Información

El presente trabajo es una contribución al estudio de la composición y dinámica de los grupos de investigación en el ámbito universitario. El enfoque novedoso que plantea es una estrategia de demarcación y análisis de grupos en perspectiva comparada entre los proyectos (inputs) y las coautorías (outputs). Combina técnicas bibliométricas y de análisis de redes sociales aplicadas a un estudio de caso: el Departamento de Bibliotecología de la Universidad Nacional de La Plata, Argentina, en el periodo 2000-2009

EPANET Input Files of New York tunnels and Pacific City used in a metamodel-based optimization study

Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude.

Análisis de la composición y dinámica de grupos de investigación en perspectiva comparada input-ouput : Estudio de caso en Bibliotecología y Ciencia de la Información

El presente trabajo es una contribución al estudio de la composición y dinámica de los grupos de investigación en el ámbito universitario. El enfoque novedoso que plantea es una estrategia de demarcación y análisis de grupos en perspectiva comparada entre los proyectos (inputs) y las coautorías (outputs). Combina técnicas bibliométricas y de análisis de redes sociales aplicadas a un estudio de caso: el Departamento de Bibliotecología de la Universidad Nacional de La Plata, Argentina, en el periodo 2000-2009

Análisis de la composición y dinámica de grupos de investigación en perspectiva comparada input-ouput : Estudio de caso en Bibliotecología y Ciencia de la Información

El presente trabajo es una contribución al estudio de la composición y dinámica de los grupos de investigación en el ámbito universitario. El enfoque novedoso que plantea es una estrategia de demarcación y análisis de grupos en perspectiva comparada entre los proyectos (inputs) y las coautorías (outputs). Combina técnicas bibliométricas y de análisis de redes sociales aplicadas a un estudio de caso: el Departamento de Bibliotecología de la Universidad Nacional de La Plata, Argentina, en el periodo 2000-2009