The Effectiveness of Alternative Preference Elicitation Procedures in Predicting Choice

1993 ◽  
Vol 30 (1) ◽  
pp. 105-114 ◽  
Author(s):  
Joel Huber ◽  
Dick R. Wittink ◽  
John A. Fiedler ◽  
Richard Miller
Keyword(s):  
Large Scale ◽  
Absolute Error ◽  
Accurate Method ◽  
Preference Elicitation ◽  
National Study ◽  
Relative Preference ◽  
Full Profile ◽  
The Mean ◽  
Combining Information ◽  
Better Than

In a large-scale national study, the authors evaluated the effectiveness of several preference elicitation techniques for predicting choices. The criteria for accuracy included both individual hit rates and a new measure, the mean absolute error predicting aggregate share using a logit choice simulator. The central finding is that hybrid models combining information from different preference elicitation tasks consistently outperform models based on one task. For example, ACA, a method that combines a self-explicated prior with relative preference measures on pairs, predicts choices better than full-profile conjoint when warmup tasks are lacking. However, there is no difference between the models if ACA's prior is combined with the full-profile information. Further, the most accurate method combines data from all three sources, suggesting that each preference elicitation technique taps a different aspect of the choice process in the validation task. Finally, full-profile conjoint is found to be significantly more accurate after rather than before, other preference elicitation tasks, implying that its performance can be improved with warmup exercises.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

The Met Office operational global ocean forecast system FOAM-ORCA12

2021 ◽  
Author(s):  
Ana Barbosa Aguiar ◽  
Jennifer Waters ◽  
Martin Price ◽  
Gordon Inverarity ◽  
Christine Pequignet ◽  
...  
Keyword(s):  
Computational Cost ◽  
Absolute Error ◽  
Global Ocean ◽  
Background Error ◽  
Verification Methods ◽  
Climate Simulations ◽  
Forecast Models ◽  
The Mean ◽  
Lower Continuous ◽  
Better Than

<div> <p>The importance of oceans for atmospheric forecasts as well as climate simulations is being increasingly recognised with the advent of coupled ocean / atmosphere forecast models. Having comparable resolutions in both domains maximises the benefits for a given computational cost. The Met Office has recently upgraded its operational global ocean-only model from an eddy permitting 1/4 degree tripolar grid (ORCA025) to the eddy resolving 1/12 degree ORCA12 configuration while retaining 1/4 degree data assimilation. </p> </div><div> <p>We will present a description of the ocean-only ORCA12 system, FOAM-ORCA12, alongside some initial results. Qualitatively, FOAM-ORCA12 seems to represent better (than FOAM-ORCA025) the details of mesoscale features in SST and surface currents. Overall, traditional statistical results suggest that the new FOAM-ORCA12 system performs similarly or slightly worse than the pre-existing FOAM-ORCA025. However, it is known that comparisons of models running at different resolutions suffer from a double penalty effect, whereby higher-resolution models are penalised more than lower-resolution models for features that are offset in time and space. Neighbourhood verification methods seek to make a fairer comparison using a common spatial scale for both models and it can be seen that, as neighbourhood sizes increase, ORCA12 consistently has lower continuous ranked probability scores (CRPS) than ORCA025. CRPS measures the accuracy of the pseudo-ensemble created by the neighbourhood method and generalises the mean absolute error measure for deterministic forecasts. </p> </div><div> <p>The focus over the next year will be on diagnosing the performance of both the model and assimilation. A planned development that is expected to enhance the system is the update of the background-error covariances used for data assimilation. </p> </div>

Relative Efficiency of Ancillary and Small Scale Units

1978 ◽  
Vol 3 (4) ◽  
pp. 279-284
Author(s):  
K.R. Shaligram
Keyword(s):  
Small Firms ◽  
Large Scale ◽  
Small Scale ◽  
Underlying Assumption ◽  
Significant Difference ◽  
End Products ◽  
Policy Measures ◽  
The Mean ◽  
Small Manufacturers ◽  
Better Than

Ancillary units are small firms manufacturing and supplying intermediate goods, typically to large firms. Several policy measures are under consideration to raise the output of the ancillary industry to the level of 15 per cent of the value of output of the large scale industry by 1985. The underlying assumption appears to be that the ancillary status enhances the prospect for the viability of the small firm. This paper examines whether ancillary units perform better than small scale units (small manufacturers of end products) under the conditions prevailing in India. The findings reveal no significant difference in the mean performance of the two classes of small firms. It also draws implications for policymakers and management from the findings.

scholarly journals Prediction by using Artificial Neural Networks and Box-Jenkins methodologies: Comparison Study

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Habib Al- Sharoot ◽  
Emaan Yousif Abdoon
Keyword(s):  
Neural Networks ◽  
Exchange Rate ◽  
Absolute Error ◽  
Percentage Error ◽  
Neural Network Approach ◽  
Decision Making Processes ◽  
The Mean ◽  
Us Dollar ◽  
Stable Economy ◽  
Better Than

The variations in exchange rate, especially the sudden unexpected increases and decreases, have significant impact on the national economy of any country. Iraq is no exception; therefore, the accurate forecasting of exchange rate of Iraqi dinar to US dollar plays an important role in the planning and decision-making processes as well as the maintenance of a stable economy in Iraq. This research aims to compare Box-Jenkins methodology to neural networks in terms of forecasting the exchange rate of Iraqi dinar to US dollar based on data provided by the Iraqi Central Bank for the period  30/01/2004 and 30/12/2014. Based on the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE) as criteria to compare the two methodologies, it was concluded that Box-Jenkins is better than neural network approach in forecasting.

scholarly journals Wie viele Schulen braucht ein fairer Vergleich? Sozialindex-basierte Adjustierungsstrategien im Vergleich

2020 ◽  
Author(s):  
Johannes Schult
Keyword(s):  
Secondary School ◽  
Large Scale ◽  
Comparison Group ◽  
School Management ◽  
Economic Index ◽  
Fair Comparison ◽  
Comparison Groups ◽  
The Mean ◽  
Adjustment Strategies ◽  
Better Than

Large-scale educational competence tests provide teachers and school management with information regarding their students’ performance. For a so-called fair comparison, a school’s mean score can be compared with the mean score of schools with a similar social composition. Similarity is usually determined by a social school index that is based on contextual factors that lie beyond the teachers’ influence. The present study compares two adjustment strategies along with three types of indices with regard to their fairness in terms of explained performance variance (R2). Each combination is modelled with 24 different comparison group sizes in order to find out how many schools are needed for a fair comparison. The analysis is based on two cohorts with 2330 elementary schools and 1261 secondary school, respectively. A socio-cultural index performs better than a socio-economic index based on georeferenced data and also better than a combined socio-cultural/socio-economic index. Using comparison groups based on index quantile splits yields slightly higher R2-values than using schools right above and below the target school on the index. If possible, the number of comparison groups should at least 20 with each group containing at least 20 schools. The results suggest that a reasonably fair comparison can be obtained efficiently while costing little, remaining robust across grades and subjects, and being comprehensible for teachers and school management personal.

Modelling of a carbon removal biological aerated filter doing partial nitrification during large-scale secondary treatment

2014 ◽  
Vol 49 (3) ◽  
pp. 245-257 ◽  
Author(s):  
Jean Bernier ◽  
Vincent Rocher ◽  
Sabrina Guerin ◽  
Paul Lessard
Keyword(s):  
Large Scale ◽  
Absolute Error ◽  
Carbon Removal ◽  
Plant Optimization ◽  
The Mean ◽  
Hourly Concentration ◽  
Aerated Filter ◽  
Media Data ◽  
Soluble Components ◽  
Composite Samples

A wastewater biofiltration model is used to assess the potential of modelling plant-sized secondary carbon removal biofilter units. Two distinct datasets collected at the Seine-Centre biofiltration plant (Colombes, France) are used. The model is first calibrated on multiple grab samples taken at different heights inside the filter media. Data from 24 hour composite samples at the unit influent and effluent over a 2 year period are then simulated. Additional data are used to estimate hourly concentration profiles from composite samples in order to correctly use both composite and grab samples during modelling. The calibrated model is in most cases able to correctly predict the general nutrient behaviour for both datasets. The results of statistical scores such as the mean error and the mean absolute error are low for soluble components and remain correct for particles during years 2008–2009. Only one parameter set containing few heavily modified values is used to obtain these results. Modelling plant-sized biofilters appears to be practical and can be useful for easily evaluating plant optimization scenarios.

Extracting icebergs freeboard from the shadows in Landsat-8 panchromatic images

2020 ◽  
Author(s):  
Zhenfu Guan ◽  
Yan Liu
Keyword(s):  
Large Scale ◽  
Accuracy Assessment ◽  
Elevation Angle ◽  
Absolute Error ◽  
Landsat 8 ◽  
High Precision Measurement ◽  
Solar Elevation ◽  
The Mean ◽  
The Antarctic ◽  
Solar Elevation Angle

<p><strong>Abstract:</strong> The iceberg freeboard is an important geometric parameter for measuring the thickness of the iceberg and then estimating its volume. Based on the fact that the iceberg can cast elongated shadow on the surface of sea ice in winter, this paper proposes a method to measure the iceberg freeboard using shadow length and the predefined or estimated solar elevation angle. Three Landsat-8 panchromatic images are selected to test our method, with center solar elevation angle of respectively 5.43°, 7.49°and 11.01° on August 29, September 7, and 16 September in 2016. Shadow lengths of five isolated tabular icebergs are automatically extracted to calculate the freeboard height. For the accuracy assessment, we use the matching points at the different time as cross validation. The results show that the measurement error of shadow length is less than one pixel. When the sun elevation angle is lower than 11.01°, the Root Mean Square Error (RMSE) of the iceberg freeboard from the panchromatic 15 m image is less than 2.0 m, and the Mean Absolute Error (MAE) is less than 1.5 m. Such experiment shows that: under the angle of low solar elevation in winter, the landsat-8 panchromatic 15 m image can be used for high-precision measurement of the iceberg freeboard, and has the potential to measure the Antarctic iceberg freeboard in large scale.</p><p><strong>Key </strong><strong>words:</strong> Antarctic, icebergs, freeboard, shadow altimetry, Landsat-8</p><p> </p>

Numerical Study of Sand Deposition and Control by Flat Solar Panels

2012 ◽  
Author(s):  
Chuanjin Lan ◽  
Zhen Li ◽  
Yanbao Ma
Keyword(s):  
Large Scale ◽  
Numerical Study ◽  
Optimal Choice ◽  
Solar Panels ◽  
Inclination Angles ◽  
Mean Wind Speed ◽  
Entrainment Process ◽  
The Mean ◽  
And Control ◽  
Better Than

To make the best use of solar energy, most solar plants are located in deserts or dry and sandy areas, where most of the sand originate in sandstorms and desertification. For large scale solar plants, the structure of the solar panels can reduce the mean wind speed greatly, thus having a great effect on the deposition and entrainment process of the sand and dust. To study the effect of installment of solar panels on wind flow, numerical simulations are applied to get the turbulent flow field in the lee of the solar panels, with inclination angles ranging from 15° to 30° and at different spacing. The results show that 30° is the optimal choice and the performance with larger spacing at 2.5 times panel length is better than the case at 1.5 times.

scholarly journals Validation of MODIS C6.1 and MERRA-2 AOD Using AERONET Observations: A Comparative Study over Turkey

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 905
Author(s):  
Midyan Aldabash ◽  
Filiz Bektas Balcik ◽  
Paul Glantz
Keyword(s):  
Absolute Error ◽  
Coefficient Of Determination ◽  
Space Agency ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
The Mean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Modern Era ◽  
Relative Root ◽  
Better Than

This study validated MODIS (Moderate Resolution Imaging Spectroradiometer) of the National Aeronautics and Space Agency, USA, Aqua and Terra Collection 6.1, and MERRA-2 (Modern-ERA Retrospective Analysis for Research and Application) Version 2 of aerosol optical depth (AOD) at 550 nm against AERONET (Aerosol Robotic Network) ground-based sunphotometer observations over Turkey. AERONET AOD data were collected from three sites during the period between 2013 and 2017. Regression analysis showed that overall, seasonally and daily statistics of MODIS are better than MERRA-2 by the mean of coefficient of determination (R2), mean absolute error (MAE), and relative root mean square deviation (RMSDrel). MODIS combined Terra/Aqua AOD and MERRA-2 AOD corresponding to morning and noon hours resulted in better results than individual sub datasets. A clear annual cycle in AOD was detected by the three platforms. However, overall, MODIS and MERRA-2 tend to overestimate and underestimate AOD, respectively, in comparison with AERONET. MODIS showed higher efficiency in detecting extreme events than MERRA-2. There was no clear relation found between the accuracy in MODIS/MERRA-2 AOD and surface relative humidity (RH).

scholarly journals Statistical Models in Estimating Air Temperature in a Mountainous Region of Greece

2017 ◽  
Vol 12 (3) ◽  
pp. 544-549 ◽  
Author(s):  
Stelios Maniatis ◽  
Kostas Chronopoulos ◽  
Aristidis Matsoukis ◽  
Athanasios Kamoutsis
Keyword(s):  
Air Temperature ◽  
Statistical Models ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Multi Layer Perceptron ◽  
Simple Linear Regression ◽  
The Mean ◽  
Of Greece ◽  
Better Than

The current work focuses on the estimation of air temperature (T) conditions in two high altitude (alt) sites (1580 m), each one at different orientation (southeast and northwest) in the mountain (Mt) Aenos in the island of Cephalonia, Greece, by using two well-known statistical models, simple linear regression (SLR) and multi-layer perceptron ( MLP), one of the most commonly used artificial neural networks. More specifically, the estimation of mean, maximum and minimum T in high alt sites was based on the respective T data of two lower alt sites (1100 m), the first at southeast and the second at northwest orientations, and was carried out separately for each orientation. The performance of both SLR and MLP models was evaluated by the coefficient of determination (R2) and the Mean Absolute Error (MAE). Results showed that the examined models (SLR and MLP) provided very satisfactory results with regard to the estimation of mean, maximum and minimum T, regarding southeast orientation (R2 ranging from 0.96 to 0.98), with mean T estimation being relatively better, as confirmed by the lowest MAE (0.83). Regarding northwest orientation, T estimation was less accurate (lower R2 and higher MAE), compared to the respective estimation of southeast orientation, but, the results were considered adequate (R2 and MAE ranging from 0.88 to 0.92 and 1.00 to 1.40, respectively). In general, the estimations of the mean T were better than those of the extreme ones (minimum and maximum T). In addition, better results (higher R2 and lower, in general, MAE) were obtained when T estimations were based on T data derived from sites located at areas with similar surroundings, as in the case of dense and tall vegetation of the sites at southeast orientation, irrespective of applied method.

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