Search or ask a question

What is Mean Squared Error (MSE)?

Expression (computer science)

Convergence (economics)

Best insight from top research papers

Mean Squared Error (MSE) is a crucial performance measure in estimation methods for systems like Multiple-Input Multiple-Output (MIMO) systems. It quantifies the average squared difference between the estimated values and the actual values. MSE plays a significant role in evaluating the accuracy and convergence properties of estimation techniques. Various methods, such as continuous-time processing and one-bit quantized systems, utilize MSE for optimization and analysis. The MSE can be influenced by parameters like regularization parameters, which impact convergence rates. Additionally, MSE expressions can be derived for different types of estimators, aiding in performance predictions and comparisons in various scenarios.

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Open access•Posted Content•DOI An Approximate MSE Expression for Maximum Likelihood and Other Implicitly Defined Estimators of Non-Random Parameters (extended version) 01 Apr 2022	Mean Squared Error (MSE) is a measure of the average squared difference between the estimator and the true parameter value, crucial for evaluating estimator performance in statistical analysis.
Journal Article•DOI On the Mean Square Error Optimal Estimator in One-Bit Quantized Systems Benedikt Fesl, Michael Koller, Wolfgang Utschick - Show less +2 more 08 Dec 2022-IEEE Transactions on Signal Processing	Mean Squared Error (MSE) is analyzed for optimal channel estimation in one-bit quantized systems, comparing the Conditional Mean Estimator (CME) to the Bussgang estimator in various scenarios.
Open access•Posted Content•DOI MMSE Signal Detection for MIMO Systems based on Ordinary Differential Equation 03 May 2022	The paper defines Mean Squared Error (MSE) as a principal detection performance measure of estimation methods for MIMO systems. It derives an analytical MSE formula and discusses its convergence properties.
Proceedings Article•DOI MMSE Signal Detection for MIMO Systems based on Ordinary Differential Equation Ayano Nakai-Kasai, Tadashi Wadayama - Show less +1 more 03 May 2022 4 Citations	The paper provides a definition of Mean Squared Error (MSE) as a principal detection performance measure for estimation methods in MIMO systems.
Open access•Proceedings Article•DOI MMSE Signal Detection for MIMO Systems based on Ordinary Differential Equation 04 Dec 2022	The paper provides an analytical formula for Mean Squared Error (MSE) in the context of estimation methods for MIMO systems. The MSE is a principal detection performance measure for these systems.

My columns

Related Questions

Does mase, mean average scaled error, higher than 1 is a indicator of misspecification?5 answersThe Mean Absolute Scaled Error (MASE) being higher than 1 is not necessarily an indicator of misspecification. While Hyndman and Koehler (2006) recommend MASE as a standard for comparing forecast accuracies, it does not imply misspecification. MASE fits within the standard statistical procedures for testing equal forecast accuracies initiated by Diebold and Mariano (1995). Other criteria that do not imply the relevant moment properties do not fit within these procedures. Therefore, MASE being higher than 1 does not automatically indicate misspecification.

What is the purpose of using Mean absolute scale (MASE) error?5 answersThe purpose of using Mean Absolute Scale Error (MASE) is to evaluate models and compare their performance. MASE is a metric that measures the accuracy of a model's forecasts by comparing them to the forecasts of a naive or benchmark model. It is particularly useful when dealing with time series data and forecasting. MASE is advantageous because it is scale-independent, meaning it can be used to compare models across different scales or units of measurement. It provides a standardized measure of forecast accuracy that is interpretable and can be easily understood by analysts and decision-makers. MASE is widely used in various fields, including economics, finance, and meteorology, to assess the quality of forecasting models and make informed decisions based on their performance.

WHAT IS rOOT mEAN SQUARED ERROR?5 answersRoot mean squared error (RMSE) is a commonly used measure of estimation error in statistics. It is calculated by taking the square root of the average of the squared differences between the predicted values and the actual values. RMSE provides a measure of how well a model or estimator fits the data, with lower values indicating better fit. It is widely used in various fields, including neuroscience and structural damage detection. In neuroscience, RMSE is used to measure the timing distortion between a target spike sequence and an externally-stimulated neuron spike sequence. In structural damage detection, RMSE is used to identify the location of damage by comparing the operational deflection shape curvature of a structure before and after damage.

Mean squared error?5 answersMean squared error (MSE) is a tool used to compare the accuracy of different estimators in linear regression models. It measures the closeness of an estimator to the true value by considering both bias (systematic deviation from the true value) and variance (variation around the expected value due to sampling variability). MSE is commonly used to evaluate the performance of estimators, such as the least squares estimator and alternative estimators like the Liu Estimator and the special case of Liu-type estimator. It has also been employed to assess visual image quality, where approximately equal bias/variance ratios are necessary for reliable comparisons. The likelihood ratio test for a specified value of MSE has been derived for normal distributions, providing a means to test hypotheses concerning MSE. Overall, MSE is a valuable tool for evaluating estimator accuracy and assessing image quality in various applications.

What is mean square displacement (MSD)? what is the function?5 answersMean square displacement (MSD) is a measure of the average distance traveled by particles over time. It is commonly used to study the movement of particles in various physical and biological systems. The function of MSD is to provide insights into the dynamics and behavior of particles in a given system. It can be used to analyze the diffusion properties of particles, determine the nature of their motion (e.g., normal or anomalous), and understand the relationship between particle movement and system parameters such as temperature or microstructure. The MSD function can be derived using different mathematical approaches, such as quantum mechanics or Levy random walks, depending on the specific system being studied.

What is the value of mass error?5 answersThe value of mass error varies depending on the specific context. In the context of mass measurements of cosmic ray particles stopping in a stack of nuclear emulsions, a standard error of less than 0.5 atomic mass units can be achieved for certain nuclei in a specific charge interval if certain conditions are met. In the context of measuring the overall mass scale in SUSY-like events at a hadron collider, errors on the masses of particles in the decay chain range from 4 GeV to 13 GeV, depending on the number of events and specific cuts applied. In the context of measuring mass ratios of molecular ions, the dominant source of error is random magnetic field fluctuations, resulting in an rms scatter of 2.6 × 10−10 in measurements of the cyclotron frequency.

See what other people are reading

What is the molecular structure of a prion protein?

The molecular structure of a prion protein involves key aspects such as metal ion binding, beta-sheet formation, glycolipid anchors, and intermolecular beta sheets. Prion proteins like PrPC can bind Cu(II) and Zn(II) ions, leading to structural transitions from polyproline II helices to beta-turn and beta-sheet structures. Additionally, infectious prions are amyloid fibrils with parallel in-register intermolecular beta sheets, where residues 95-227 form the ordered fibril core, while glycans and glycolipid anchors project from the fibril's surfaces. The globular domain of PrPC can dimerize through domain swap or non-covalent association, with potential implications for the conversion process to the pathogenic form. Understanding these structural features is crucial for developing anti-prion therapies targeting the stabilization of the native conformation of PrPC.What emotions do expressions in images convey?

Facial expressions in images convey a wide range of emotions, including happiness, sadness, fear, disgust, surprise, and anger. These emotional cues play a crucial role in human affective perception and can significantly influence how individuals interpret multimedia content. Studies have shown that automatic reactions to facial expressions are not solely based on the emotional content but can be better understood through quantitative descriptions of low-level image features like spatial frequency and Histograms of Oriented Gradients (HOG). Additionally, the emotional content of images is essential in fields like opinion mining, sentiment analysis, and Human-Computer Interaction (HCI), where identifying emotions in images can enhance user experience and diversify results.What is the mean ambient temperature for kumasi?

The mean ambient temperature of Kumasi has been a subject of various studies, reflecting changes over time due to factors such as urbanization, loss of greenery, and climate change. From the data provided, it is evident that Kumasi has experienced a gradual increase in mean surface temperature over the years. Specifically, a study conducted over a 55-year period from 1960 to 2015 observed a mean surface temperature increase of 1.2°C, indicating a slight but notable change in the city's micro-climate due to significant land use changes. Another study covering a 42-year period from 1976 to 2018 found a 2°C rise in mean annual temperature, further corroborating the trend of increasing temperatures in Kumasi. Moreover, thermal simulation analysis of different wall materials in the warm-humid climate of Kumasi showed indoor temperatures ranging between 29.8 to 30.1°C, depending on the orientation and material of the buildings, which indirectly reflects the ambient temperature conditions within the city. This is consistent with the broader trend of rising temperatures in urban areas like Kumasi, as highlighted by temperature variability studies that have shown an increase in warm days and a general rise in minimum temperatures over a 29-year period from 1986 to 2015. The loss of green spaces, as assessed in a study that analyzed land cover change and temperature recordings from 2000 to 2016, has also contributed to the temperature dynamics in Kumasi, with a noted increase in temperature over the decade from 2000 to 2010, although a slight drop was observed in the following six years. Given these findings, while specific mean ambient temperatures are not directly provided, the data suggests a gradual increase in Kumasi's mean ambient temperature over the past decades, influenced by urban development, loss of vegetation, and climate change factors.Why is the standardized beta better than the unstandardized beta in the coefficient table?

Standardized beta coefficients are preferred over unstandardized beta coefficients in the coefficient table because they provide more informative comparisons across variables, especially when variables lack a natural metric. Standardized coefficients allow for the comparison of predictor-criterion relationships across studies with different units of measure. Additionally, standardized effect sizes, such as those used in item response models, offer a more consistent comparison across items with varying response distributions, enhancing the interpretability of the results. Techniques like the multivariate delta method and heteroskedasticity-consistent estimators have been developed to estimate standard errors and confidence intervals for standardized regression coefficients, aiding in accurately assessing the sampling variability of these coefficients.Why some people agree that old jeepneys should be phased out and be replaced by electric jeepneys instead? Why?

Some people advocate for phasing out old jeepneys and replacing them with electric jeepneys due to various reasons. Firstly, old jeepneys are associated with negative issues like lack of maintenance, safety concerns, environmental impact, and inefficient operations. Transitioning to electric jeepneys can address these issues by providing a sustainable, environmentally friendly, and safer mode of transportation. Additionally, electric vehicles, including electric jeepneys, contribute to reducing greenhouse gas emissions, which is crucial for environmental conservation and combating climate change. By utilizing renewable energy sources like solar power to charge electric vehicles, operators can extend operation time, reduce costs, and decrease carbon emissions, making the switch to electric jeepneys a viable and beneficial option.How to use gams to run cge model?

To run a Computable General Equilibrium (CGE) model using GAMS, users can follow specific steps outlined in various resources. Firstly, users should be familiar with CGE modeling and GAMS. GAMS is a user-friendly software that transforms mathematical models into solver-compatible representations, making it popular in academic and commercial settings. Additionally, there are open-source frameworks like CGEBox that support different functional forms for demand and trade sector modeling in GAMS, allowing for regional and global CGE analysis. Furthermore, tools like VEMS can assist in setting up CGE models by specifying stocks, flows, and generating social accounting matrices, with GAMS used for solving and calibrating the models. By leveraging these resources and understanding the GAMS software, users can effectively run CGE models for various economic analyses.Do abandonded spaces negatively impact the surrounding communities?

Abandoned spaces have a detrimental impact on surrounding communities. They contribute to increased crime rates, decreased property values, and a decline in the health and safety of residents. Vacant and abandoned housing not only affects the quality of life but also promotes violence and further abandonment, leading to severe consequences for public safety and neighborhood disrepair. In vulnerable communities, the lack of access to public spaces due to neglect and abandonment deprives residents, especially children and adolescents, of essential outdoor environments for play and community interaction. While temporary appropriation practices in abandoned buildings may offer short-term opportunities for experimentation and cultural activities, they often fail to address the underlying issues of neglect and disinvestment in these spaces. Abandoned properties serve as lingering reminders of economic downturns, perpetuating neighborhood disinvestment and blight.What kind of cells are optimal set of cells or candidate of architecture in NAS?

The optimal set of cells or candidate architectures in Neural Architecture Search (NAS) includes various types of cells that have shown promising results in different tasks. Research has highlighted the effectiveness of novel backbone cell architectures for recurrent neural networks, incorporating operations with two inputs like the Hadamard product. Additionally, existing NAS algorithms tend to favor architectures with wide and shallow cell structures, which demonstrate fast convergence due to smooth loss landscapes and accurate gradient information. Moreover, focusing on component block levels and integrating dimension matching modules have been proposed to enhance the search for optimal network structures, leading to computational efficiency in NAS tasks like image restoration. Furthermore, Pareto-Optimal Progressive NAS combines time-accuracy optimization to identify architectures with competitive accuracy and reduced training time.Did Bulgaria's economy change after the fall of communism?

After the fall of communism in Bulgaria in 1989, significant changes occurred in the country's economy. Privatization led to a shift in ownership, with the state's direct ownership in top companies decreasing substantially, while foreign investors became major shareholders in many companies. Economic growth in Bulgaria has slowed down recently, although the country has been converging towards more advanced economies, with GDP growth averaging 2.3% and reaching half of the OECD's average disposable income per capita by 2019. However, Bulgaria's progress towards ecological transformation has been slow and ineffective, with studies showing inertia in transitioning to an ecological economy. Additionally, regulatory gaps have hindered the development of specific social enterprises in Bulgaria, impacting their potential for institutional change and compliance with policies.What is the evolutionary relationship between Rattus norvegicus and humans?

The evolutionary relationship between Rattus norvegicus (Norway rat) and humans is multifaceted. Studies have shown that rats and humans share a long historical proximity, with rodents being present wherever humans reside. Research indicates that both species possess the ability to detect temporal regularities in sound sequences, suggesting ancient evolutionary roots for this trait. Furthermore, phylogeographic and zooarchaeological studies have highlighted how the spread of commensal rat species, including R. norvegicus, alongside humans, can provide insights into human migration, urban settlements, trade networks, and competition dynamics. These findings underscore a complex evolutionary interplay between Rattus norvegicus and humans, shaped by shared environments and interactions over time.What are the technological and operational risks of unmanned underwater vessel's fuel-reforming system for fuel cells in underwater vehicles?

The fuel-reforming system for fuel cells in unmanned underwater vehicles poses technological and operational risks. Technological risks include the need for efficient hydrogen generation and purification, as well as the challenge of reforming various fuels to supply hydrogen for fuel cells. Operational risks involve controlling the pressure of the reformed gas supplied to the hydrogen purifier to ensure optimal performance. Additionally, the system must effectively separate liquid water from the gaseous phase in the output flow to maintain functionality. These risks highlight the complexity and critical nature of fuel-reforming systems in underwater vehicles, emphasizing the importance of thorough risk analysis and mitigation strategies for successful and sustainable operation.