Estimation Method Research Articles

Demirjian's and Cameriere's Methods for the Assessment of Dental Age Estimation in Children from a Southern Brazilian City.

The chronological age estimation of living individuals is a crucial part of forensic practice and clinical practice, such as in orthodontic treatment. It is well-known that methods for age estimation in living children should be tested on different populations. Ethnic affiliations in Brazil are divided into several major groups depending on the region, with the south of Brazil being known for its German immigration. (1) Background: This study aimed to evaluate the correlation between chronological age and dental age using Demirjian's method and Cameriere's method in a group of children from Joinville, South Brazil to investigate if both methods can be used to estimate dental age in this population. (2) Methods: The sample consisted of 229 panoramic radiographs (119 were males and were 110 females) from Brazilian children (ages ranging from 6 to 12 years). The chronological age at the time of the panoramic radiographic exam was calculated for each child. The dental age was estimated according to Demirjian's method and Cameriere's method. All continuous data were tested for normality by using the Shapiro-Wilk test. The Pearson correlation coefficient test was applied. An alpha of 5% (p < 0.05) was used for all analyses. (3) Results: The mean chronological age was 8.75 years. According to Demirjian's method, the mean dental age was 9.3 years, while according to Cameriere's method, the mean dental age was 8.66 years. A strong correlation between chronological age and dental age according to Demirjian (r = 0.776 and p < 0.0001) and Cameriere (r = 0.735 and p < 0.0001) was observed for both genders. (4) Conclusions: Both methods presented a good correlation with chronological age in the studied population and could be used to assess dental age in this population.

Overview of Open-Loop Frequency Estimation Techniques for Grid Synchronization of Single-Phase Power Converters

Open-loop synchronization techniques allow to overcome the stability issues to which closed-loop techniques are subject. Most open-loop techniques differ in their Quadratic Signal Generator and their frequency estimation method. In this paper, an overview of the main frequency estimation methods for single-phase power systems is presented. A description of each technique with their structure for numerical implementation is carried out. A comparative study of those frequency estimation methods is performed according to three test scenarios in order to evaluate the robustness of each method. Recommendations for the selection of the technique is given according to the simulation results.

Monocular Absolute Depth Estimation from Motion for Small Unmanned Aerial Vehicles by Geometry-Based Scale Recovery.

In recent years, there has been extensive research and application of unsupervised monocular depth estimation methods for intelligent vehicles. However, a major limitation of most existing approaches is their inability to predict absolute depth values in physical units, as they generally suffer from the scale problem. Furthermore, most research efforts have focused on ground vehicles, neglecting the potential application of these methods to unmanned aerial vehicles (UAVs). To address these gaps, this paper proposes a novel absolute depth estimation method specifically designed for flight scenes using a monocular vision sensor, in which a geometry-based scale recovery algorithm serves as a post-processing stage of relative depth estimation results with scale consistency. By exploiting the feature correspondence between successive images and using the pose data provided by equipped navigation sensors, the scale factor between relative and absolute scales is calculated according to a multi-view geometry model, and then absolute depth maps are generated by pixel-wise multiplication of relative depth maps with the scale factor. As a result, the unsupervised monocular depth estimation technology is extended from relative depth estimation in semi-structured scenes to absolute depth estimation in unstructured scenes. Experiments on the publicly available Mid-Air dataset and customized data demonstrate the effectiveness of our method in different cases and settings, as well as its robustness to navigation sensor noise. The proposed method only requires UAVs to be equipped with monocular camera and common navigation sensors, and the obtained absolute depth information can be directly used for downstream tasks, which is significant for this kind of vehicle that has rarely been explored in previous depth estimation studies.

Using Stable Nitrogen Isotope Ratios from Human Skeletal Muscle Tissue for Postmortem Interval (PMI) Estimation (Part 1)

Human skeletal muscle tissue was analyzed for stable nitrogen isotope analysis (δ15N) to test the applicability of changes in values (Δ15N) through the putrefaction process for postmortem interval (PMI) estimation. Seasonal differences in Δ15N values of decom-posing skeletal muscle tissue were analyzed from 20 human donors at the University of Tennessee Anthropology Research Facility. This study revealed that change in the δ15N ratios of decomposing muscle tissue has potential as a novel method for PMI estimation. In winter months, when identifiable muscle tissue can be retained on a body for months, the data indicate that there is a systematic increase in Δ15N values with increasing temperatures when putrefaction progresses faster with values shifting as much as 3.0‰. This initial study indicates there is value in further method development of this quantitative approach to PMI estimation for recently deceased individuals, but further work is needed. While the analysis only requires a small amount of tissue, has a user- friendly collection and analysis protocol, and is less subjective than visual methods, there is still significant research to be done prior to widespread applications as a PMI estimation method. Understanding variability in δ15N across decomposing muscle tissue groups, individual variation (i.e., muscle mass, disease, body chem-istry), and an accurate approximation for a perimortem δ15N value necessary to calculate Δ15N all need to be further researched prior to generation of a reliable regression formula and error rates.

Analytical quality by design approach to develop an eco-friendly RP-HPLC method for estimation of irbesartan in chitosan polymeric nanoparticles: forced degradation studies and assessment of in vitro release mathematical modelling.

Irbesartan is an angiotensin converting enzyme blocker, primarily utilized for the management of hypertension and the mitigation of diabetic nephropathy progression. The present study introduces rapid, robust and environmentally sustainable reverse phase high performance liquid chromatography (RP-HPLC) validated under the analytical quality by design (AQbD) framework according to ICH guidelines. Utilizing a central composite design, the method's systemic optimization was achieved, ensuring reproducibility and accuracy. Chromatographic separation was accomplished utilizing an ethanol and sodium acetate buffer (60 : 40 v/v) isocratic mobile phase system on a zorbax sb C18 column, with a flow rate of at 0.6 mL min-1. Studies on forced degradation outlined stability of irbesartan and its degradation processes, enhancing our understanding of its chemical robustness under varied conditions. Complementing the green chemistry paradigm, the method's environmental impact was critically assessed, affirming its alignment with sustainability objectives. The validated method proved pivotal in determining the percent entrapment and loading efficiency of the formulated nanoparticles and holds potential for application in biological matrices. Furthermore, the encapsulation of IRB within chitosan nanoparticles was explored to assess release kinetics and enhance bioavailability. This study not only advances the analytical sciences by merging eco-friendly practices with method development but also broadens the applicative landscape of HPLC methodologies in drug delivery research.

Can input reconstruction be used to directly estimate uncertainty of a dose prediction U-Net model?

The reliable and efficient estimation of uncertainty in artificial intelligence (AI) models poses an ongoing challenge in many fields such as radiation therapy. AI models are intended to automate manual steps involved in the treatment planning workflow. We focus in this study on dose prediction models that predict an optimal dose trade-off for each new patient for a specific treatment modality. They can guide physicians in the optimization, be part of automatic treatment plan generation or support decision in treatmentindication. Most common uncertainty estimation methods are based on Bayesian approximations, like Monte Carlo dropout (MCDO) or Deep ensembling (DE). These two techniques, however, have a high inference time (i.e., require multiple inference passes) and might not work for detecting out-of-distribution (OOD) data (i.e., overlapping uncertainty estimate for in-distribution (ID) and OOD). In this study, we present a direct uncertainty estimation method and apply it for a dose prediction U-Net architecture. It can be used to flag OOD data and give information on the quality of the doseprediction. Our method consists in the addition of a branch decoding from the bottleneck which reconstructs the CT scan given as input. The input reconstruction error can be used as a surrogate of the model uncertainty. For the proof-of-concept, our method is applied to proton therapy dose prediction in head and neck cancer patients. A dataset of 60 oropharyngeal patients was used to train the network using a nested cross-validation approach with 11 folds (training: 50 patients, validation: 5 patients, test: 5 patients). For the OOD experiment, we used 10 extra patients with a different head and neck sub-location. Accuracy, time-gain, and OOD detection are analyzed for our method in this particular application and compared with the popular MCDO andDE. The additional branch did not reduce the accuracy of the dose prediction model. The median absolute error is close to zero for the target volumes and less than 1% of the dose prescription for organs at risk. Our input reconstruction method showed a higher Pearson correlation coefficient with the prediction error (0.620) than DE (0.447) and MCDO (between 0.599 and 0.612). Moreover, our method allows an easier identification of OOD (no overlap for ID and OOD data and a Z-score of 34.05). The uncertainty is estimated simultaneously to the regression task, therefore requires less time and computationalresources. This study shows that the error in the CT scan reconstruction can be used as a surrogate of the uncertainty of the model. The Pearson correlation coefficient with the dose prediction error is slightly higher than state-of-the-art techniques. OOD data can be more easily detected and the uncertainty metric is computed simultaneously to the regression task, therefore faster than MCDO or DE. The code and pretrained model are available on the gitlab repository: https://gitlab.com/ai4miro/ct-reconstruction-for-uncertainty-quatification-of-hdunet.

Local Walsh-average regression for spatial autoregression single index varying coefficient models

. In real life, a large number of variables are spatially correlated in adjacent regions in various fields, such as finance, sociology, ecology, and geographic information systems. And the factors affecting these variables often have both linear and non linear relationships with them, how to accurately depict the relationship between them with a spatial regression model is a significant work. In order to further accurately describe the relationship between explanatory variables and response variables, we establish a spatial autoregressive single index varying coefficient model. The least squares method is the main method of model estimation but it is not robust enough when the parameter distribution is not normal. We present a new estimation method based on local Walsh average-regression. Under some general assumptions, we establish the asymptotic properties of the parametric and non parametric estimators. We further verify the accuracy and efficiency of the estimators under the condition of non normal error distribution through numerical experiments. In addition, the robustness of the proposed method is verified when the spatial weight matrix is interfered and when the bandwidth is changed within a certain range. Finally, we demonstrate the accuracy of forecasting by our proposed model compared with traditional spatial autoregressive model through real data analysis.

Pano2Geo: An efficient and robust building height estimation model using street-view panoramas

Building height serves as a crucial parameter in characterizing urban vertical structure, which has a profound impact on urban sustainable development. The emergence of street-view data offers the opportunity to observe urban 3D scenarios from the human perspective, benefiting the estimation of building height. In this paper, we propose an efficient and robust building height estimation model, which we call the Pano2Geo model, by precisely projecting street-view panorama (SVP) coordinates to geospatial coordinates. Firstly, an SVP refinement stratagem is designed, incorporating NENO rules for observation quality assessment from four aspects: number of buildings, extent of the buildings, number of nodes, and orthogonal observations, followed by the application of the art gallery theorem to further refine the SVPs. Secondly, the Pano2Geo model is constructed, which provides a pixel-level projection transformation from SVP coordinates to 3D geospatial coordinates for locating the height features of buildings in the SVP. Finally, the valid building height feature points in the SVP are extracted based on a slope mutation test, and the 3D geospatial coordinates of the building height feature points are projected using the Pano2Geo model, so as to obtain the building height. The proposed model was evaluated in the city of Wuhan in China, and the results indicate that the Pano2Geo model can accurately estimate building height, with an average error of 1.85 m. Furthermore, compared with three state-of-the-art methods, the Pano2Geo model shows superior performance, with only 10.2 % of buildings have absolute errors exceeding 2 m, compared to the Map-image-based (27.2 %), Corner-based (16.8 %), and Single-view-based (13.9 %) height estimation methods. The SVP refinement method achieves optimal observation quality with less than 50 % of existing SVPs, leading to highly efficient building height estimation, particularly in areas of a high building density. Moreover, the Pano2Geo model exhibits robustness in building height estimation, maintaining errors within 2 m even as building shape complexity and occlusion degree increase within the SVP. Our source dataset and code are available at https://github.com/Giser317/Pano2Geo.git.

The burden of cirrhosis mortality by county, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities

Cirrhosis is responsible for substantial health and economic burden in the USA. Reducing this burden requires better understanding of how rates of cirrhosis mortality vary by race and ethnicity and by geographical location. This study describes rates and trends in cirrhosis mortality for five racial and ethnic populations in 3110 US counties from 2000 to 2019. We estimated cirrhosis mortality rates by county, race and ethnicity, and year (2000-19) using previously validated small-area estimation methods, death registration data from the US National Vital Statistics System, and population data from the US National Center for Health Statistics. Five racial and ethnic populations were considered: American Indian or Alaska Native (AIAN), Asian or Pacific Islander (Asian), Black, Latino or Hispanic (Latino), and White. Cirrhosis mortality rate estimates were age-standardised using the age distribution from the 2010 US census as the standard. For each racial and ethnic population, estimates are presented for all counties with a mean annual population greater than 1000. From 2000 to 2019, national-level age-standardised cirrhosis mortality rates decreased in the Asian (23·8% [95% uncertainty interval 19·6-27·8], from 9·4 deaths per 100 000 population [8·9-9·9] to 7·1 per 100 000 [6·8-7·5]), Black (22·8% [20·6-24·8], from 19·8 per 100 000 [19·4-20·3] to 15·3 per 100 000 [15·0-15·6]), and Latino (15·3% [13·3-17·3], from 26·3 per 100 000 [25·6-27·0] to 22·3 per 100 000 [21·8-22·8]) populations and increased in the AIAN (39·3% [32·3-46·4], from 45·6 per 100 000 [40·6-50·6] to 63·5 per 100 000 [57·2-70·2] in 2000 and 2019, respectively) and White (25·8% [24·2-27·3], from 14·7 deaths per 100 000 [14·6-14·9] to 18·5 per 100 000 [18·4-18·7]) populations. In all years, cirrhosis mortality rates were lowest among the Asian population, highest among the AIAN population, and higher in males than females for each racial and ethnic population. The degree of heterogeneity in county-level cirrhosis mortality rates varied by racial and ethnic population, with the narrowest IQR in the Asian population (median 8·0 deaths per 100 000, IQR 6·4-10·4) and the widest in the AIAN population (55·1, 30·3-78·8). Cirrhosis mortality increased over the study period in almost all counties for the White (2957 [96·9%] of 3051 counties) and AIAN (421 [88·8%] of 474) populations, but in a smaller proportion of counties for the Asian, Black, and Latino populations. For all racial and ethnic populations, cirrhosis mortality rates increased in more counties between 2000 and 2015 than between 2015 and 2019. Cirrhosis mortality increased nationally and in many counties from 2000 to 2019. Although the magnitude of racial and ethnic disparities decreased in some places, disparities nonetheless persisted, and mortality remained high in many locations and communities. Our findings underscore the need to implement targeted and locally tailored programmes and policies to reduce the burden of cirrhosis at both the national and local level. US National Institutes of Health (Intramural Research Program, National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; Intramural Research Program, National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research).

SVR-based method for fixed effects interval-valued panel models

. This article studies a class of fixed effects interval-valued panel models in which the response variables are intervals and the explanatory variables are single data. We propose an SVR-based estimation method to estimate unknown parameters and smoothing functions. The proposed method utilizes the kernel trick to map the original data (also called input data) to a high-dimensional feature space, which is more likely to achieve linearity than in the original space. Moreover, the proposed method is resistant to the influence of outliers. Monte Carlo experiments validate that the proposed models have good fitting accuracy and prediction performance, while the empirical application shows that the proposed models work efficiently on finite samples.

Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction

Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival forest (RSF) and conditional inference forest (Cforest) were employed to analyse the breast cancer datasets. The goal of this study is to apply these methods in prediction of breast cancer progression and compare their performances based on two different estimation methods: the bootstrap estimation and the bootstrap .632 estimation. The model performance was evaluated in concordance index (C-index) and prediction error curves (pec) for discrimination. The Cox PH model has a lower C-index and bigger prediction error compared to the RSF and the Cforest approach for both datasets. The analysis results of GBSG2 and METABRIC datasets reveal that the RSF and the Cforest algorithms provide non-parametric alternatives to Cox PH model for estimation of the survival probability of breast cancer patients.

Water residence time controls seasonal nitrous oxide budget in a semi-enclosed bay: Insights from an improvement estimation method

This study developed an estimation method for the N2O budget using 15N stable isotope labeling techniques, a dual-layer model and a box model, which was used to elucidate the underlying dynamics of N2O accumulation in Zhanjiang Bay. The results showed that although the net input of N2O during the rainy season was 2.36 times higher than that during the dry season, the overall N2O concentration was only 66.6 % of that during the dry season due to the extended water residence time in the dry season. Our findings highlighted that water residence time was the key factor for the N2O emission, and a longer water residence time was unfavorable for the efflux of N2O through hydrodynamic processes and was more conducive to the production and accumulation of N2O within the bay. This research enhanced our comprehension of N2O dynamics and provided crucial insights for refining nitrogen management strategies and mitigation efforts.

Estimation of potential social support requirement for tuberculosis patients in India.

Providing social support to tuberculosis (TB) patients is a recommended strategy as households having TB patients find themselves in a spiral of poverty because of high cost, huge income loss and several other economic consequences associated with TB treatment. However, there are few examples of social support globally. The Indian government introduced "Nikshay Poshan Yojana" scheme in 2018 to provide nutritional support for all registered TB patients. Financial incentive of Indian Rupee (INR) 500 (US$6) per month was proposed to be transferred directly to the registered beneficiaries' validated bank accounts. We examined the reach, timing, amount of benefit receipt, and the extent to which the benefit alleviated catastrophic costs (used as a proxy to measure the impact on permanent economic welfare as catastrophic cost is the level of cost that is likely to result in a permanent negative economic impact on households) by interviewing 1482 adult drug-susceptible TB patients from 16 districts of four states during 2019 to 2023, using the methods recommended by the World Health Organisation for estimating household costs of TB nationally. We also estimated the potential amount of social support required to achieve a zero catastrophic cost target. At the end of treatment, 31%-54% study participants received the benefit. 34%-60% of TB patients experienced catastrophic costs using different estimation methods and the benefit helped 2% study participants to remain below the catastrophic cost threshold. A uniform benefit amount of INR 10000 (US$127) for 6 months of treatment could reduce the incidence of catastrophic costs by 43%. To improve economic welfare of TB patients, levels of benefit need to be substantially increased which will have considerable budgetary impact on the TB programme. Hence, a targeted rather than universal approach may be considered. To maximise impact, at least half of the revised amount should be given immediately after treatment registration.

Research on Inter-Satellite Laser Ranging Scale Factor Estimation Methods for Next-Generation Gravity Satellites

The scale factor serves as a ruler for converting raw phase measurements into physical displacements and significantly impacts the preprocessing of data from the Laser Ranging Interferometer (LRI) in laser ranging systems. In the current GRACE Follow-On (GRACE-FO) mission for low–low tracking gravity satellites, most of the existing LRI scale factor estimation algorithms heavily rely on cross-calibration between instantaneous/biased ranges from the Ka-Band Ranging Interferometer (KBR) and the LRI. However, due to the nonlinearity of the objective function (which includes terms involving the product of scale and time shifts), the scale factor estimation may absorb errors from timing noise. Moreover, future gravity missions or gravity detection tasks may no longer incorporate KBR ranging instruments. To address these challenges, this paper proposes an energy-based method for scale factor estimation using inter-satellite baseline solutions. Comparative analysis indicates that the proposed method effectively disentangles two parameters in the objective function and can be applied in scenarios where KBR data are unavailable, demonstrating promising prospects for practical application. The experimental results show that when the KBR validation residuals are lower than 0.8 mm, the SYSU LRI1B V01 products exhibit residuals below the payload design accuracy thresholds in the frequency band of 2 mHz to 0.1 Hz. Additionally, the stability of the scale factors obtained from the baseline can reach 10−7. Although this is still below the required precision of better than 10−8 for the laser frequency stability in next-generation gravity satellites, advancements in orbit determination technology and the enhanced stability of GPS receivers offer potential for future precision improvements. Currently, this method appears suitable for roughly extracting the scale factor as a stochastic mean over several months or serving as a backup validation strategy for future missions, but it is not well suited to measure day-to-day variations.

Flexible Bayesian estimation of incubation times.

The incubation period is of paramount importance in infectious disease epidemiology as it informs about the transmission potential of a pathogenic organism and helps to plan public health strategies to keep an epidemic outbreak under control. Estimation of the incubation period distribution from reported exposure times and symptom onset times is challenging as the underlying data is coarse. We develop a new Bayesian methodology using Laplacian-P-splines that provides a semi-parametric estimation of the incubation density based on a Langevinized Gibbs sampler. A finite mixture density smoother informs a set of parametric distributions via moment matching and an information criterion arbitrates between competing candidates. Algorithms underlying our method find a natural nest within the EpiLPS package, which has been extended to cover estimation of incubation times. Various simulation scenarios accounting for different levels of data coarseness are considered with encouraging results. Applications to real data on COVID-19, MERS and Mpox reveal results that are in alignment with what has been obtained in recent studies. The proposed flexible approach is an interesting alternative to classic Bayesian parametric methods for estimation of the incubation distribution.

Moment estimation of uncertain autoregressive model and its application in financial market

Uncertain autoregressive model is a powerful analytical tool that uses uncertainty theory to predict future values based on previously observed values. In the study of uncertain autoregressive model, one of the core problems is how to estimate the unknown parameters and uncertain disturbance term in the model. In this paper, a moment estimation method for uncertain autoregressive model is proposed to determine these unknown parameters and uncertain disturbance term. Following that, the uncertain hypothesis test is used to verify the suitability of the estimated uncertain autoregressive model. In addition, we also provide a case study of Disney stock prices to illustrate the advantages of moment estimation method over other statistical inference methods.

State of energy estimation of lithium-ion battery based on long short-term memory optimization Adaptive Cubature Kalman filter.

State of energy (SOE) is an important parameter to ensure the safety and reliability of lithium-ion battery (LIB) system. The safety of LIBs, the development of artificial intelligence, and the increase in computing power have provided possibilities for big data computing. This article studies SOE estimation problem of LIBs, aiming to improve the accuracy and adaptability of the estimation. Firstly, in the SOE estimation process, adaptive correction is performed by iteratively updating the observation noise equation and process noise equation of the Adaptive Cubature Kalman Filter (ACKF) to enhance the adaptive capability. Meanwhile, the adoption of high-order equivalent models further improves the accuracy and adaptive ability of SOE estimation. Secondly, Long Short-term Memory (LSTM) is introduced to optimize Ohmic internal resistance (OIR) and actual energy (AE), further improving the accuracy of SOE estimation. Once again, in the process of OIR and AE estimation, the iterative updating of the observation noise equation and process noise equation of ACKF were also adopted to perform adaptive correction and enhance the adaptive ability. Finally, this article establishes a SOE estimation method based on LSTM optimized ACKF. Validate the LSTM optimized ACKF method through simulation experiments and compare it with individual ACKF methods. The results show that the ACKF estimation method based on LSTM optimization has an SOE estimation error of less than 0.90% for LIB, regardless of the SOE at 100%, 65%, and 30%, which is more accurate than the SOE estimation error of ACKF alone. It can be seen that this study has improved the accuracy and adaptability of LIB's SOE estimation, providing more accurate data support for ensuring the safety and reliability of lithium batteries.

A compensation method for PMSM sensorless control with parameter identification considering SMO observation error

AbstractSensorless control of permanent magnet synchronous motor (PMSM) can increase the reliability of electric actuators of more electrical aircraft. Numerical online parameter estimation method will enhance the performance for sensorless control methods based on sliding mode observer (SMO). However, numerical online parameter identification methods typically assume zero rotor position estimation error to ensure full rank of the identification equation set. This assumption actually leads to errors in parameter identification, resulting in steady‐state errors in rotor position estimation. This paper focuses on a compensation method for PMSM online parameter identification that takes into account rotor position estimation error. This method is used for sensorless control based on SMO, using model reference adaptive systems for online parameter identification, and no additional parameter adjustment required. Validation is conducted on a PMSM experimental platform with maximum load of 15kW. The experimental results of 1000 r/min steady state operation proved the effectiveness of the method, and the dynamic experiments with 5 and 15kW loads proved that the method has no significant adverse effect on the system dynamics.

Marginalized LASSO in the low-dimensional difference-based partially linear model for variable selection

The difference-based partially linear model is an appropriate regression model when both linear and nonlinear predictors are present in the data. However, when we want to optimize the weights using the difference-based method, the problem of variable selection can be difficult since low-variance predictors present a challenge. Therefore, this study aims to establish a novel methodology based on marginal theory to tackle such mixed relationships successfully, emphasizing variable selection in low dimensions. We suggest using a marginalized LASSO estimator with a penalty term that is not as severe and related to the difference order. As part of our numerical analysis of small sample performance, we undertake comprehensive simulation experiments to numerically demonstrate the strength of our proposed technique in estimation and prediction compared to the LASSO under a low-dimensional setup. This is done so that we can numerically demonstrate the strength of our proposed method in estimation and prediction. The bootstrapped method is utilized to evaluate how well our proposed prediction method performs when examining the King House dataset.

Marginalized LASSO in the low-dimensional difference-based partially linear model for variable selection

The difference-based partially linear model is an appropriate regression model when both linear and nonlinear predictors are present in the data. However, when we want to optimize the weights using the difference-based method, the problem of variable selection can be difficult since low-variance predictors present a challenge. Therefore, this study aims to establish a novel methodology based on marginal theory to tackle such mixed relationships successfully, emphasizing variable selection in low dimensions. We suggest using a marginalized LASSO estimator with a penalty term that is not as severe and related to the difference order. As part of our numerical analysis of small sample performance, we undertake comprehensive simulation experiments to numerically demonstrate the strength of our proposed technique in estimation and prediction compared to the LASSO under a low-dimensional setup. This is done so that we can numerically demonstrate the strength of our proposed method in estimation and prediction. The bootstrapped method is utilized to evaluate how well our proposed prediction method performs when examining the King House dataset.