Equal Weight Method Research Articles

Can composite performance measures predict survival of patients with colorectal cancer?

To assess the relationship between long-term colorectal patient survival and methods of calculating composite performance scores. The Taiwan Cancer Database was used to identify patients who underwent bowel resection for colorectal adenocarcinoma between 2003 and 2004. Patients were assigned to one of three cohorts based on tumor staging: cohort 1, colon cancer stage < III; cohort 2, colon cancer stage III; cohort 3, rectal cancer. A composite performance score (CPS) was calculated for each patient using five different aggregating methods, including all-or-none, 70% standard, equal weight, analytic hierarchy process (AHP), and principal component analysis (PCA) algorithms. The relationships between CPS and five-year overall, disease-free, and disease-specific survivals were evaluated by a Cox proportional hazards model. A goodness-of-fit analysis for all five methods was performed using Akaike's information criterion. A total of 3272 colorectal cancer patients (cohort 1, 1164; cohort 2, 790; cohort 3, 1318 patients) with a mean age of 65 years were enrolled in the study. Bivariate correlation analysis showed that CPS values from the equal weight method were highly correlated with those from the AHP method in all cohorts (all P < 0.05). Multivariate Cox hazards analysis showed that CPS values derived from equal weight and AHP methods were significantly associated with five-year survivals of patients in cohorts 1 and 2 (all P < 0.05). In these cohorts, higher CPS values suggested a higher probability of five-year survival. However, CPS values derived from the all-or-none method did not show any significant process-outcome relationship in any cohort. Goodness-of-fit analyses showed that CPS values derived from the PCA method were the best fit to the Cox proportional hazards model, whereas the values from the all-or-none model showed the poorest fit. CPS values may highlight process-outcome relationships for patients with colorectal cancer in addition to evaluating quality of care performance.

Gas Emission Prediction of Coal Mine Based on Evidence Theory Combining with Neural Network

In order to improve the prediction accuracy of gas emission, propose a prediction method of evidence theory combining with neural network. According to the experimental data of gas emission, three different particle swarm optimization-neural network models are used for the initial prediction. And use the BP and RBF network to get the credibility of model by analyzing forecasting errors and its influence factors. Then the evidence theory is used to obtain the weights of combination model, realize the gas emission combination forecasting. Examples results show that the error of evidence theory is less than error of the neural network and equal weight method, and it is suitable for gas emission prediction of coal mine.

Multi-model ensemble forecasting and glider path planning in the Mid-Atlantic Bight

During the first two weeks of November 2009, a field experiment was conducted in the Mid-Atlantic Bight region to demonstrate a coastal ocean observatory that can collect observations from heterogeneous platforms and forecast fields from four different ocean models, provide multi-model ensemble forecasts based on either an equal weighting (EQ) or objective weighting (OBJ) method, and use model forecasts in a path planning system to relocate autonomous gliders. This experiment is a prototype for the command and control component of cyberinfrastructure of the Ocean Observatories Initiative funded by the National Science Foundation. The four individual models use different forcing fields, boundary conditions and data assimilation techniques, and have resolutions varying from 2km to 15km. Our results indicate that for sea surface temperature and surface currents, the OBJ ensemble outperforms the four individual models, while the EQ ensemble can also provide an effective way to improve individual model forecasts. In terms of glider path planning, the OBJ ensemble has a performance similar to the best individual model, which has the finest horizontal resolution. This field experiment demonstrates the first-ever use of ensemble current forecasts to guide glider path planning in the context of real-time data collection and ocean model forecasting.

Weighting for what? A comparison of two weighting methods for measuring urban competitiveness

This paper examines how different weighting methods affect the results of urban competitiveness ranking. Using a hierarchical indicator system that considers economic, social, and environmental dimensions of the competitiveness of Chinese cities as a case study, it shows that principal component analysis (PCA) produces rankings of urban competitiveness that are significantly different from the alternative equal weighting (EW) method. Moreover, PCA generates problematic weights for some indicators and assigns a small weight to the environmental performance of cities. The paper investigates these problematic results and argues for the necessity of deliberating more consciously on appropriate weighting methods in the construction of composite indicators in urban competitiveness studies and beyond.

Optimizing line intercept sampling and estimation for feral swine damage levels in ecologically sensitive wetland plant communities

Ecological sampling can be labor intensive, and logistically impractical in certain environments. We optimize line intercept sampling and compare estimation methods for assessing feral swine damage within fragile wetland ecosystems in Florida. Sensitive wetland sites, and the swine damage within them, were mapped using GPS technology. Evenly spaced parallel transect lines were simulated across a digital map of each site. The length of each transect and total swine damage under each transect were measured and percent swine damage within each site was estimated by two methods. The total length method (TLM) combined all transects as a single long transect, dividing the sum of all damage lengths across all transects by the combined length of all transect lines. The equal weight method (EWM) calculated the damage proportion for each transect line and averaged these proportions across all transects. Estimation was evaluated using transect spacings of 1, 3, 5, 10, 15, and 20 m. Based on relative root mean squared error and relative bias measures, the TLM produced higher quality estimates than EWM at all transect spacings. Estimation quality decreased as transect spacing increased, especially for TLM. Estimation quality also increased as the true proportion of swine damage increased. Diminishing improvements in estimation quality as transect spacings decreased suggested 5 m as an optimal tradeoff between estimation quality and labor. An inter-transect spacing of 5 m with TLM estimation appeared an optimal starting point when designing a plan for estimating swine damage, with practical, logistical, economic considerations determining final design details.

The Application of Fuzzy Variable Sets Theory in Eutrophication Evaluation for Urban Lake

Eutrophication is one of the most serious water quality problems in the world. Urban lake eutrophication evaluation with the aim of improving urban water environment quality will provide foundations for urban water environment management and remediation. Considering that lake eutrophication evaluation involves many indexes, many rankings and interval values, fuzzy variable sets theory is applied. Based on the premise that the same mathematics model is used, index weights are determined by means of entropy value method, equal weight method and pure threshold value method. Relative membership degree of each index belonging to each ranking can be calculated with relative difference function model. At last, the stability of feature values of classification corresponding to three methods is analyzed and grade of each sample is determined. This model is applied to Lake Dongchang. Three lake regions all belong to the fifth ranking, namely eutrophic. It basically corresponds to reality. So it can be indicated that the proposed model is reasonable and practical.

The Application of Fuzzy Variable Sets Theory in Eutrophication Evaluation for Urban Lake

Eutrophication is one of the most serious water quality problems in the world. Urban lake eutrophication evaluation with the aim of improving urban water environment quality will provide foundations for urban water environment management and remediation. Considering that lake eutrophication evaluation involves many indexes, many rankings and interval values, fuzzy variable sets theory is applied. Based on the premise that the same mathematics model is used, index weights are determined by means of entropy value method, equal weight method and pure threshold value method. Relative membership degree of each index belonging to each ranking can be calculated with relative difference function model. At last, the stability of feature values of classification corresponding to three methods is analyzed and grade of each sample is determined. This model is applied to Lake Dongchang. Three lake regions all belong to the fifth ranking, namely eutrophic. It basically corresponds to reality. So it can be indicated that the proposed model is reasonable and practical.

Optimum estimator for SPOT-pan and ALS-lidar intensity image registration via tie points

The characteristics of a Système Pour d’Observation de la Terre (SPOT) panchromatic image of a geographic area such as an airfield are different from those of an airborne laser scanner lidar intensity image of the same area. This study investigates the feasibility of locating tie points shared by the target (airborne lidar-intensity) and search (spaceborne SPOT-pan) images. At an approximate point position, image segmentation and enhancement are employed so that a pair of target and search window images tend to resemble each other. The similarity is then measured by utilizing a least-squares image matching algorithm which involves variance-component estimation. The proposed method has a 3–15% higher success rate than an equal-weight matching method. Coordinate differences of the conjugate image points serve as reference value to be used in the least-squares collocation method for pixel-by-pixel transformation. The image registration accuracy from a SPOT-pan to a lidar intensity image is estimated to be ±1.0 pixel.

가구 패널조사에서의 가중치 조정에 관한 연구

미국 노동통계청에서 사용하고 있는 BLS 방법의 효율성과 민감성에 관한 연구 결과에 의하면 표본 틀(Sample frame) 자료와 조사된 자료의 상관관계가 높을수록 BLS 무응답 보정 효과는 커지는 것으로 알려져 있다 (이석진과 신기일, 2008). 그러나 표본 틀 자료와 조사된 자료의 상관계수가 층별로 크기가 다른 경우, BLS 보정 효과는 달라질 수 있다. 따라서 일반적으로 실시되는 표본 설계에서는 층화추출 방법이 사용되기 때문에 각 층의 표본 크기와 상관계수가 다른 경우의 BLS 보정 효과률 살펴보는 것은 매우 중요하다. 본 논문에서는 층의 표본 크기와 상관계수 그리고 무응답 비율에 따른 BLS 무응답 보정 효과를 살펴보았다. 이를 위해 사용된 자료는 노동부의 월별 자료인 2007년 매월노동통계 자료이다. The panel survey is need to have a more concern about a response due to a secession and non-response of a sample. And generally a population is not fixed and continuously changed. Thus, the rotation sample design can be used by the method replacing the panel research. This paper is the study of comparison to equal weight method, Duncan weight, Design weight method, weight share method in rotation sample design. More specifically, this paper compared variance estimators about the existing each method for the efficiency comparison, and to compare the precision using the relative efficiency gain by the Coefficient Variance(CV) after getting the design weight from the actual data.

Review and new evidence on composite innovation indicators for evaluating national performance

The purpose of this contribution is to present a survey of the recent developments in constructing composite science and technology (S&T) indicators on a national level as well as new evidence of the variability of such S&T indicators which opens the gateway to “country-tuning”. It has become standard practice to combine several indicators for science, technology, and innovation to form composite numbers. Especially in the light of this variability, two questions arise. Firstly, are the results (especially rankings) stable with respect to weights? Secondly, is there hope to define “economically” reasonable weights? In order to provide answers to these questions, we use data from the European Innovation Scoreboard 2005 (EIS 2005) to exemplify our reasoning. Concerning the first question, we give genuine evidence on the existence of immense variability, possibly invalidating the results. Further, we also show that even existing and well-accepted methods, like equal weighting, Benefit of the Doubt weighting (BoD) and principal component analysis weighting (PCA) may lead to drastically differing results. Concerning the second question we will demonstrate that by each composite indicator weighting a set of shadow prices is implied expressing one indicator in terms of another. Whether the weights are sensible should be evaluated on the basis of these shadow prices. It turns out that those implied by EIS 2005 contain strange peculiarities. After that we plead for more care in constructing composite indicators. Especially weights should be chosen on the basis of shadow prices, rather than, say, by equal weighting or other automatic methods. Lastly, we discuss the merit of composite indicators and argue that they have a valuable communication and competition function, but they should be accompanied by multidimensional representations, which provide the basis for the construction of policy measures.

Web-based CBR system applied to early cost budgeting for pavement maintenance project

With the growth of transportation networks in developing countries, the cost-efficacy control of maintenance operations has become critical to the infrastructure asset management after highway construction. To effectively manage numerous projects annually with limited resources, it is necessary to accurately estimate costs and leave a trail of project information during the process of making maintenance project selection decisions. This paper outlines the development of a case-based reasoning (CBR) expert prototype system that compares historical data at the work item-level across the case library. This study attempts to determine preliminary project cost with readily available information rapidly based on previous experience of pavement maintenance related construction to assist decision makers in project screening and budget allocation. Various CBR modeling approaches were presented and assessed in terms of their mean absolute prediction error rates. Design and implementation of a web-based CBR system is demonstrated in this study to efficiently handle the attribute and case similarity computation and the results are displayed using browsers. Furthermore, weighting attributes employed in the CBR system were compared via eigenvector and equal weighting methods for estimating aggregate cost and component costs. Historical generic pavement maintenance projects were gathered from the Taiwan transportation agencies and used for model training and testing. Furthermore, k-fold cross-validation was employed to verify the CBR estimating system. The analytical results demonstrate the ability of the system to estimate the item-level cost of pavement maintenance projects with the satisfactory precision during the conceptual project phase. The developed prototype web-based CBR system can efficiently provide timely and accurate information in an efficient way and provide an alternative estimation tool that can be combined with other evaluation criteria, such as indexes of pavement serviceability and structure strength, to improve the decision making in relation to budget allocation.

Memory and Reasoning Abilities Assessed by the Universal Nonverbal Intelligence Test: A Reliable Component Analysis (RCA) Study

The Universal Nonverbal Intelligence Test (UNIT) is an important advancement in intelligence assessment, but the equal-weighting method used to compute scores for the memory and reasoning abilities results in some psychometric weaknesses. The equally weighted memory and reasoning scores are highly correlated, leading to a lack of discriminant validity, a potential lack of incremental validity, and poor reliability for their score differences. Reliable component analysis (RCA) was performed on the UNIT normative data to allow for the computation of reliable, uncorrelated memory and reasoning scores. The RCA scores were highly replicable across samples and ages, have good convergent validity, and have greater discriminant validity than the equally weighted scores. Furthermore, the difference between the memory and reasoning scores is much more reliable when using RCA. This results in narrower confidence intervals around difference scores and more powerful tests of the null hypothesis that there is no true difference between memory and reasoning abilities.

Improving the fuzzy system performance by fuzzy system ensemble

This paper proposes a fuzzy system ensemble (FSE) that can improve the system performance in non-linear and complex problems. Each fuzzy system in the FSE is built from two design stages, each stage of which is performed by different genetic algorithms (GAs). The first stage generates a fuzzy rule base that covers as many of the training examples as possible. The second stage builds fine-tuned membership functions that make the system error as small as possible. These two stages are repeated independently upon the different partitions of input-output variables. The system error will be reduced further by invoking the FSE that combines multiple fuzzy systems with an equal system-error weighting method where the weight constant is inversely proportional to the FS's error. Applications of the FSE to both the truck backer-upper control and the Mackey-Glass chaotic time-series prediction are presented. For the truck control problem, control performance of the proposed method is compared with the approach of Wang and Mendel [IEEE Transactions on System, Man, and Cybernetics 22 (1992) 1414] in terms of either the rate of successful controls reaching to the goal or the average traveling distance. For the chaotic time-series prediction problem, prediction accuracy of the proposed method is compared with that of other fuzzy or neural network predictors in terms of non-dimensional error index (NDEI).

Nonnegativity restricted least squares combinations

In combining reasonably efficient forecasts of a time series, nonnegative weights are intuitively meaningful and their constrained least squares estimators are known to have desirable theoretical properties. It is analytically shown that whether such inequality (or equality) constraints will result in gains in efficiency depends on the number of observations in the fit sample and the mean level of the fit set. Next, the performances of the combination forecasts of a macroeconomic time series obtained using Nonnegativity Restricted Least Squares (NRLS) and other combination methods are exhaustively compared. It is shown that NRLS combination models are more efficient, more robust, and less sensitive to sample size than Ordinary Least Squares, Equality Restricted Least Squares, minimum-variance, and outperformance models. They are more efficient than historical weighting and equal weighting methods only for large samples. Two heuristic NRLS algorithms are shown to yield equally accurate and robust combined forecasts.