Scale Estimation Research Articles

Limited Width of Tropical Cyclone–Induced Baroclinic Geostrophic Response

Abstract Despite the large radius (R17) of gale-force wind of a tropical cyclone (TC), the observed TC-induced effects on mesoscale and large-scale ocean via the baroclinic geostrophic response are found to have a limited cross-track width; this strange but important phenomenon is interpreted here. Driven by the wind stress curl (WSC), the TC-induced geostrophic response is in fact regulated by along-track integration of the WSC (AIWSC). Constrained by atmospheric TC dynamics, the violent winds outside the radius (Rmax) of maximum wind of any TC must have nearly zero WSC. Consequently, the AIWSC function can be fit as a boxcar function with an extraordinarily large positive value between ±Rmax about the track. Based on this boxcar function, the theoretical estimate of the cross-track length scale of the baroclinic geostrophic response, Ld + Rmax, is presented, where Ld is the first-mode baroclinic Rossby deformation radius. Further, this scale is validated by numerical experiments to well explain the width of the altimetry-observed geostrophic response induced by any TC. Evidently, Ld + Rmax is far smaller than R17 and thus the baroclinic geostrophic response generally has a limited width. This study implies that, although for a TC the violent winds outside Rmax are generally ∼90% of all winds, in an open ocean these winds may be useless to perturb the ocean interior due to the nearly zero WSC. Significance Statement Despite the large radius of gale-force wind of a tropical cyclone, the effects of a tropical cyclone on mesoscale and large-scale ocean are confined in a limited cross-track width; this strange but important phenomenon is interpreted here. In essence, the effects are exerted by the wind stress curl rather than by the wind stress. However, constrained by atmospheric dynamics, a tropical cyclone has most of the positive wind stress curl in the inner core and nearly zero wind stress curl far away from the inner core. Consequently, albeit violent, the winds outside the inner core cannot make an appreciable contribution to the physical processes below the mixed layer.

Differential Treatment Effects of Subgroup Analyses in Phase 3 Oncology Trials From 2004 to 2020

Subgroup analyses are often performed in oncology to investigate differential treatment effects and may even constitute the basis for regulatory approvals. Current understanding of the features, results, and quality of subgroup analyses is limited. To evaluate forest plot interpretability and credibility of differential treatment effect claims among oncology trials. This cross-sectional study included randomized phase 3 clinical oncology trials published prior to 2021. Trials were screened from ClinicalTrials.gov. Missing visual elements in forest plots were defined as a missing point estimate or use of a linear x-axis scale for hazard and odds ratios. Multiplicity of testing control was recorded. Differential treatment effect claims were rated using the Instrument for Assessing the Credibility of Effect Modification Analyses. Linear and logistic regressions evaluated associations with outcomes. Among 785 trials, 379 studies (48%) enrolling 331 653 patients reported a subgroup analysis. The forest plots of 43% of trials (156 of 363) were missing visual elements impeding interpretability. While 4148 subgroup effects were evaluated, only 1 trial (0.3%) controlled for multiple testing. On average, trials that did not meet the primary end point conducted 2 more subgroup effect tests compared with trials meeting the primary end point (95% CI, 0.59-3.43 tests; P = .006). A total of 101 differential treatment effects were claimed across 15% of trials (55 of 379). Interaction testing was missing in 53% of trials (29 of 55) claiming differential treatment effects. Trials not meeting the primary end point were associated with greater odds of no interaction testing (odds ratio, 4.47; 95% CI, 1.42-15.55, P = .01). The credibility of differential treatment effect claims was rated as low or very low in 93% of cases (94 of 101). In this cross-sectional study of phase 3 oncology trials, nearly half of trials presented a subgroup analysis in their primary publication. However, forest plots of these subgroup analyses largely lacked essential features for interpretation, and most differential treatment effect claims were not supported. Oncology subgroup analyses should be interpreted with caution, and improvements to the quality of subgroup analyses are needed.

Implementation and observability analysis of visual-inertial-wheel odometry with robust initialization and online extrinsic calibration

Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visual-inertial odometry algorithm based on sliding window tightly fusing data from the above three sensors. Firstly we propose an IMU-odometer pre-integration approach utilizing complete IMU measurements and wheel encoder readings, to make scale estimation more accurate in subsequent 4-degrees of freedom (DoF) optimization. Secondly we develop an original initialization module where encoder readings are fully utilized to refine gravity direction and provide an initial value for camera pose in real scale. Thirdly, we design a computationally efficient online extrinsic calibration method by fixing the linearization point for the rotational component of IMU-odometer extrinsic parameters, which is deployed depending on the convergence of accelerometer bias. Fourthly, we give an observability analysis of our optimization based approach under more general assumption. Extensive experiments are performed on two sets of data in various scenes, bringing the state-of-the-art visual odometry and visual-inertial odometry algorithms into comparison. Experimental results prove the overwhelmingly better performance of our proposed approach on the above two sets of data, as well as the robustness of our initialization module and the improvement resulted from online extrinsic calibration.

Catchment-Wide Groundwater Budget for the Inkomati-Usuthu Water Management Area in South Africa.

In South Africa, approximately 98% of the predicted total surface water resources are already being used up. Consequently, the National Water Resource Strategy considers groundwater to be important for the future planning and management of water resources. In this case, quantifying groundwater budgets is a prerequisite because they provide a means for evaluating the availability and sustainability of a water supply. This study estimated the regional groundwater budgets for the Inkomati-Usuthu Water Management Area (Usuthu, Komati, Sabie-Sand, and Crocodile) using the classical hydrological continuity equation. The equation was used to describe prevailing feedback loops between groundwater draft, recharge, baseflow, and storage change. The results were coarser scale estimates which, beforehand, were derived from the 2006 study. In the years to follow, groundwater reliance intensified and there was also the historic 2015/2016 drought. This inevitably led to an increased draft while the rest of the components of the groundwater budgets experienced decreases. Both Crocodile and Sabie-Sand experienced groundwater storage depletion which led to reduced baseflow and groundwater availability, while groundwater recharge contrarily increased due to capture. Conversely, the other two catchments experienced relatively lower drafts with correspondingly higher groundwater availability and recharge while storage change was positive. The results highlighted the need for adaptive water management whose effectiveness relies on predictive studies. Consequently, future models should be developed to capture the spatial and temporal dynamism of the natural groundwater budget due to climate change, water demands, and population growth predictions.

EVALUATION OF THE RELIABILITY AND CONCORDANCE OF VISUAL WEIGHT ASSESSMENT IN WATER BUFFALO FEMALES

<p><strong>Background.</strong> For the development of reproductive, nutritional and health programmes, estimation of animal body weight (BW) is a fundamental tool in herd management. <strong>Objective. </strong>To evaluate of the reliability and concordance<strong> </strong>of visual weight assessment in water buffalo females. <strong>Methodology. </strong>Data on visually estimated body weight (VM) and actual body weight (BW) were recorded for 229 female water buffaloes. BW was recorded using a digital scale and visual estimation BW was taken as the average of three observations made by three observers. The measurements obtained by each of the different estimation methods were also compared with the observed weights by inspecting the paired Bland-Altman plots prior to logarithmic transformation. <strong>Results. </strong>Correlations between observed BW and mean predicted BW for visual methods showed a positive and significant relationship (P<0.001), with an r value of 0.95. According to the intraclass correlation coefficient (ICC), there was a high degree of reliability in the visual estimates of buffalo weight, due to the similarity in the mean and dispersion of each observer's estimates. Both the Bland-Altman plot and the ICC show that there is a high level of concordance between the buffalo weights obtained by the visual and real methods. <strong>Implications.</strong> The result of the present study showed that visual assessment highlights the ability of livestock handlers to have a very accurate estimation of BW in female water buffaloes. <strong>Conclusion.</strong> The results of this study show that visual assessment highlights the ability of livestock handlers to visually estimate BW in female water buffaloes with high reliability and concordance with the animal weighbridge method.</p>

Problems and prospects of development of public-private partnership in the Republic of Kazakhstan: Political analysis

Relevance. Public-private partnership (PPP) is considered as a priority in Kazakhstan's state policy for achieving strategic plans, fostering political stability, and boosting the economy by attracting private investment in key sectors. Thus, studying the PPP issues and prospects is vital, necessitating a comprehensive economic perspective and integration with other societal spheres of public life. Purpose. The study aims to explore the political aspect of PPP development in Kazakhstan and seeks to assess the extent of influence exerted by various factors on the PPP mechanism. Additionally, the study intends to identify measures for enhancing the PPP framework, with particular attention to the relative importance of factors impacting its progress. Methodology. The study's empirical basis involved a PEST analysis of PPP issues, utilizing expert questionnaires from 5 political scientists, 5 economists, 5 sociologists, and 5 technology specialists. An expert survey with scaling estimates was conducted, followed by an analysis of these assessments. Results. The results of the study reveal ways to enhance the PPP mechanism by examining the specific impacts of political, economic, social, and technological factors. The study is theoretically grounded in neocorporatism, which advocates for the systematic involvement of interest groups in shaping state policies that connect government and business entities. The hypothesis was tested by evaluating the influence of these factors on PPP development, identifying key challenges, and outlining future prospects for PPPs in Kazakhstan. Conclusions. Based on the findings of the PEST analysis, it is evident that updating the legislative framework to align with current requirements is crucial for enhancing the efficiency of PPP initiatives. Improving PPP project quality necessitates prioritizing projects addressing fundamental societal needs and critical issues. Keywords: strategic partnership; business; state; neo-corporatism; information technology; democratic society

Is having your cell phone the key to happiness, or does it really matter? Evidence from a randomized double-blind study

BackgroundAffect can influence people’s perceptions, decisions, and the way they make sense of an experience. Some studies show that having one’s cell phone removed will lead to negative emotional reactions, while others have found no significant impact on how we feel. In this paper we investigate the impact of cell phone possession and removal on participant’s affective state.MethodsWe use a randomized double-blind procedure to examine whether cell phone removal enhances negativity, promotes positivity, or is emotionally inconsequential. We measure affect using a PANAS self-report scale as well as a less transparent temporal-estimation procedure.ResultsOur findings suggest that cell phone possession or removal has no influence on a person’s affective state.ConclusionsMeasured through both the PANAS self-report scale and temporal estimation task, affect remained consistent regardless of cell phone possession. These results suggest that cell phones may not carry the emotional weight often attributed to them. This finding challenges a common theme revolving around the negative emotional impact of cell phones and technology. Consequently, these findings may have important implications for the generally perceived notion that cell phones are having a negative effect on people’s emotions.

From Pixels to Precision: A Survey of Monocular Visual Odometry in Digital Twin Applications.

This survey provides a comprehensive overview of traditional techniques and deep learning-based methodologies for monocular visual odometry (VO), with a focus on displacement measurement applications. This paper outlines the fundamental concepts and general procedures for VO implementation, including feature detection, tracking, motion estimation, triangulation, and trajectory estimation. This paper also explores the research challenges inherent in VO implementation, including scale estimation and ground plane considerations. The scientific literature is rife with diverse methodologies aiming to overcome these challenges, particularly focusing on the problem of accurate scale estimation. This issue has been typically addressed through the reliance on knowledge regarding the height of the camera from the ground plane and the evaluation of feature movements on that plane. Alternatively, some approaches have utilized additional tools, such as LiDAR or depth sensors. This survey of approaches concludes with a discussion of future research challenges and opportunities in the field of monocular visual odometry.

Quantification of active layer depth at multiple scales in Interior Alaska permafrost

Much of Interior Alaska is underlain by permafrost that has been thawing at an unprecedented rate. Top-down expansion of the seasonally thawed ‘active layer’ and development of thermokarst features are increasing across the landscape. This can be attributed primarily due to a warming climate and disturbances like wildfires which have accelerated summer season permafrost thaw. Quantification of active-layer thickness (ALT) is critical to understanding the response of permafrost terrains to these disturbances. ALT measurements are time consuming, and point based. As a result, there are large uncertainties in ALT estimates at regional/global scales (100 km2 or larger) using field scale (1 m2) measurements as direct inputs for calibrating/validating large scale process-based or statistical/empirical models. Here we developed a framework to link field scale ALT measurements with satellite observations to a regional scale (100 km2) via an intermediary upscaling of field scale ALT to the local scale (1 km2) with fine-resolution airborne hyperspectral and light detection and ranging data, thus leading to a characterization of ALT across space and time at multiple scales. We applied an object-based machine learning ensemble approach to upscale field scale (1 m2) measurements to the local (1 km2) and regional scale (100 km2) and achieved encouraging results across three permafrost experimental sites in Interior Alaska that represent a variety of terrain types. Our study demonstrates that generating local scale data products is an effective approach to bridge the gap with field scale measurements and regional scale estimations as it seeks to reduce upscaling uncertainty.

Direct numerical simulation of temporally evolving stratified wakes with ensemble average

Direct numerical simulations are conducted for temporally evolving stratified wake flows at Reynolds numbers from $10\,000$ to $50\,000$ and Froude numbers from $2$ to 50. Unlike previous studies that obtained statistics from a single realization, we take ensemble averages among 80–100 realizations. Our analysis shows that data from one realization incur large convergence errors. These errors reduce quickly as the number of statistical samples increases, with the benefit of ensemble average diminishing beyond 40–60 realizations. The data with ensemble average allow us to test the previously established scalings and arrive at new scaling estimates. Specifically, the data do not support power-law scaling in the centreline velocity deficit $U_0$ beyond the near wake. Its decay rate increases continuously from 0.1 at the onset of the non-equilibrium regime until the end of our calculations without reaching any asymptote. Additionally, while no power-law scalings could be found in the wake width ( $L_H$ ) and wake height ( $L_V$ ) in the late wake, $L_H\sim (Nt)^{1/3}$ is a good working approximation of the wake's horizontal size, where $N$ is buoyancy frequency and $t$ is time. Besides the low-order statistics, we also report the transverse integrated terms and the vertically integrated terms in the turbulent kinetic energy budget equation as a function of the vertical and transverse coordinates. The data indicate that there are two peaks in the vertically integrated production and transport terms, and one peak when the two terms are integrated horizontally.

On the response of daily precipitation extremes to local mean temperature in the Yangtze River basin

This study explores the response of daily extreme precipitation (PEX) to local mean temperatures (T) of the previous 0-day (T0), 1-day (T1), and 1 to 9-days (T1–9) in the Yangtze River basin (YRB) using the non-stationarity test, binning method and copula theory. Results show that the annual scaling rate of PEX with T during 1961–2020 exhibits positive, negative and positive changing trends from southwest to northeast with the maximum magnitudes in the west, and experiences significant abrupt change only at 14.1% grids. The PEX-T scaling relation in the entire study period is predominantly characterized by a peak pattern over the YRB, with precipitation peaking at about 8 °C in the western highlands and at 25 °C in the east-central lowlands. However, the use of T1–9 yields a positive PEX-T pattern at more grids compared to T1 and T0, which highlights the important role of atmospheric moisture residence time in minimizing cooling artefacts in the scaling estimation. Generally, the full scaling rate of PEX with T in the study period is larger in the west-central part but smaller in the eastern YRB, and more sensitive to antecedent temperatures, with the ranges of −8–20%/°C, −8–30%/°C and −10–30%/°C against T0, T1 and T1–9, respectively. As precipitation intensifies, the full scaling decreases in plateau-mountain areas but increases in low-lying regions, gradually approaching the Clausius-Clapeyron (CC) scaling and exhibiting a “super-CC, CC-like and sub-CC scaling” pattern from west to east. Additionally, heavier precipitation events are most likely to occur at higher local temperatures in most areas even with peak structure. These findings provide new insights into the PEX-T relationships, which is conducive to understanding future changes in precipitation extremes and assessing precipitation-related risks under climate change.

Magnetotransport and Fermi surface segmentation in Pauli limited superconductors

We report the first theoretical investigation of the spectroscopic, electrical and optical transport signatures of d-wave Pauli limited superconductors, based on a non perturbative numerical approach. We demonstrate that the high magnetic field low temperature regime of these materials host a finite momentum paired superconducting phase. Multi-branched dispersion spectra with finite energy superconducting gaps, anisotropic segmentation of the Fermi surface and spatial modulations of the superconducting order characterizes this finite momentum paired phase and should be readily accessible through angle resolved photo emission spectroscopy, quasiparticle interference and differential conductance measurements. Based on the electrical and optical transport properties we capture the non Fermi liquid behavior of these systems at high temperatures, dominated by local superconducting correlations and characterized by resilient quasiparticles which survive the breakdown of the Fermi liquid description. We map out the generic thermal phase diagram of the d-wave Pauli limited superconductors and provide for the first time the accurate estimates of the thermal scales corresponding to the: (a) loss of (quasi) long range superconducting phase coherence (T c), (b) loss of local pair correlations (T pg), (c) breakdown of the Fermi liquid theory (T max) and cross-over from the non Fermi liquid to the bad metallic phase (T BR). Our thermal phase diagram mapped out on the basis of the spectroscopic and transport properties are found to be in qualitative agreement with the experimental observations on CeCoIn5 and κ-BEDT, in terms of the thermodynamic phases and the phase transitions. The results presented in this paper are expected to initiate important transport and spectroscopic experiments on the Pauli limited d-wave superconductors, providing sharp signatures of the finite momentum Cooper paired state in these materials.

Differential evolution with evolutionary scale adaptation

The performance of differential evolution (DE) algorithm heavily depends on the evolutionary scale, which is controlled by the generation operations including mutation, crossover and the control parameters including mutation factor and crossover rate. Adjusting the evolutionary scale to suit different types of problems is a critical yet challenging open question in DE research. To efficiently address this issue, this paper proposes a novel DE based on evolutionary scale adaptation, termed as ESADE. First, a successful scale estimation mechanism is proposed to measure the appropriate evolutionary scale by utilizing the successful evolutionary feedback from the solution space provided by the trial vectors and the target vectors. With the appropriate evolutionary scale, an evolutionary scale adaptation mechanism pre-selects the closest or the farthest trial vector from each target vector, which corresponds to a small or large evolutionary scale respectively to match the search requirements of different evolutionary stages. The effectiveness of the ESA method is demonstrated by performance comparisons with each single baseline strategy, state-of-the-art DEs and state-of-the-art multi-strategy methods on 29 benchmark functions with three dimensionalities. Further applications on several real-world optimization problems also reveal the competitive performance of ESADE.

Practical Considerations in Dose Extrapolation from Animals to Humans.

Animal studies are an important component of drug product development and the regulatory review process since modern practices have been in place, for almost a century. A variety of experimental systems are available to generate aerosols for delivery to animals in both liquid and solid forms. The extrapolation of deposited dose in the lungs from laboratory animals to humans is challenging because of genetic, anatomical, physiological, pharmacological, and other biological differences between species. Inhaled drug delivery extrapolation requires scrutiny as the aerodynamic behavior, and its role in lung deposition is influenced not only by the properties of the drug aerosol but also by the anatomy and pulmonary function of the species in which it is being evaluated. Sources of variability between species include the formulation, delivery system, and species-specific biological factors. It is important to acknowledge the underlying variables that contribute to estimates of dose scaling between species.

A robust alternative to the Lilliefors test of normality

The Lilliefors test of normality is a popular and easy-to-explain method for testing whether a sample comes from a normal distribution. Unfortunately, since it relies on the sample mean and sample standard deviation for estimating the parameters of the normal distribution, the Lilliefors test is quite sensitive to the presence of outliers. Contrarily to what could be expected, the substitution of the estimators of location and scale by robust alternatives still does not suffice for obtaining a robust method when either the number of outliers or the sample size is large. In this paper, we propose an actual robust alternative relying on classical and data-driven trimming techniques. The presented test depends on the choice of a subsetting technique, of which we explore three possibilities, and of one parameter, which models the robustness of the test in the presence of outliers. As expected, the choice of parameter is a delicate issue since the gain in robustness comes at the price of a reduced power against some types of alternatives.

Estimation for Refined Carbon Storage of Urban Green Space and Minimum Spatial Mapping Scale in a Plain City of China

Current cities are not concrete jungles and deserts with sparse vegetation. Urban green space (UGS) appears widely in human activity areas and plays an important role in improving the human living environment and accumulates carbon storage. However, given the scattered distribution of UGS, studies on both the refined spatial estimation of carbon storage and appropriate mapping scale are still lacking. Taking the downtown area of Kaifeng, China, as the study area, this study verified the i-Tree Eco model on the basis of a field survey and accurately estimated the spatial carbon storage of UGS by combining it with remote sensing data, and finally, we obtained the minimum spatial mapping scale of UGS carbon storage by scaling. The results showed that (1) the total area of UGS in study area was 26.41 km2, of which the proportion of total area of residential area and park green spaces was about 50%. The area of UGS per capita in the study area is 40.49 m2. (2) Within the 123 survey samples, the proportion of communities with tree–shrub–herbs structure was the highest, 51.22%. The average carbon density was 5.89 kg m−2, among which the park, protective and square green spaces had the highest carbon density in all land use types. (3) The total carbon storage of UGS in the study area was 114,389.17 t, and the carbon storage of UGS per capita was 175.39 kg. Furthermore, the scaling analysis showed that 0.25 km spatial resolution was the minimum spatial scale for UGS carbon storage mapping. This study improves our understanding of urban carbon storage, highlights the role and potential of UGS in carbon neutrality, and clarifies the importance of estimating urban carbon storage at appropriate scales. This study is also of great significance for rationally understanding the terrestrial carbon cycle in urban areas and improving regional climate simulations.

Comparing bluegill growth among newly constructed, renovated, and established flood-control reservoirs in southeastern Nebraska

Perrion MA, Lebsock JR, Werner JP, Blank AJ, Miller BT. 2024. Comparing bluegill growth among newly constructed, renovated, and established flood-control reservoirs in southeastern Nebraska. Lake Reserv Manage. 40:57–65. Multiple reservoirs have been constructed in the last 10 yr in southeastern Nebraska for flood control and recreational purposes. In addition, other existing reservoirs have undergone major renovations in the same time period to remove sediment and aquatic invasive species. This situation created the opportunity to compare bluegill (Lepomis machrochirus) growth between newly constructed, renovated, and established reservoirs. This study analyzed bluegill growth data from 8 different flood-control reservoirs in southeastern Nebraska in 2021 and 2022. The study objectives were to (1) determine aging precision of scales and otoliths in bluegill and (2) evaluate bluegill growth among new, renovated, and established flood-control reservoirs. For all bluegill collected during the study, exact percent agreement for otolith ages between 2 readers was 89.6%, and percent agreement was 99.1% within 1 yr of age. For scales, exact percent agreement was 48.0% and 84.3% within 1 yr. Mean lengths at age for all reservoir types were similar for age-2 bluegill, whereas age-3 bluegill had longer mean lengths in renovated reservoirs than in either new or established reservoirs. Mean lengths of age-4 bluegill from new and renovated reservoirs were similar, but greater than mean lengths of age-4 bluegill in established reservoirs. Results of this study further demonstrate the greater precision of whole view otolith aging for bluegills compared to scale estimates and will benefit future managers making management decisions for bluegills. Managers can also use information ascertained from bluegill growth metrics to further manage bluegill populations based on reservoir age and management objectives.

Concept Graph Neural Networks for Surgical Video Understanding.

Analysis of relations between objects and comprehension of abstract concepts in the surgical video is important in AI-augmented surgery. However, building models that integrate our knowledge and understanding of surgery remains a challenging endeavor. In this paper, we propose a novel way to integrate conceptual knowledge into temporal analysis tasks using temporal concept graph networks. In the proposed networks, a knowledge graph is incorporated into the temporal video analysis of surgical notions, learning the meaning of concepts and relations as they apply to the data. We demonstrate results in surgical video data for tasks such as verification of the critical view of safety, estimation of the Parkland grading scale as well as recognizing instrument-action-tissue triplets. The results show that our method improves the recognition and detection of complex benchmarks as well as enables other analytic applications of interest.

Robust regression modelling for inflation factor in the Indonesian economy development

Inflation is a general and continuous increase in the prices of goods and services over a while. Inflation is measured by the Consumer Price Index (CPI) indicator to calculate the average price change of a package of goods and services households consume over time. Inflation in Indonesia is not in a precarious condition. However, it also states that inflation in Indonesia could rise. This rise can affect the sustainable development goals (SDGs). Therefore, Indonesia needs to have inflation data scrutinized every year. This study aims to examine and show the factors that affect inflation. Robust regression is an important method to analyze data contaminated by outliers and provide more flexible results. So that it can produce robust regression models and determine what factors significantly affect inflation in Indonesia by the research objectives, this study’s research type is experimental research with literature studies. The variable used is Inflation in Indonesia, which is the dependent variable of the study, and the other four independent variables are export value, interest rate, money supply, and exchange rate. After that, a robust regression model with maximum likelihood type (M) estimation, scale (S) estimation, and least median of squares (LMS) estimation will be obtained, and the best model will be selected. The results show that the least median of squares (LMS) estimation is the best with the acquisition of an Akaike information criteria value of -390.1363. Furthermore, the high and low inflation in 20182022 is influenced by interest rates, money supply and export value.

Implementing a New Scale Technique in the M-Estimation Method to Estimate Parameters of Multiple Linear Regression: Simulation Study

The goal of this study is to develop a new technique for estimating the parameters of a multiple linear regression by using M-estimation based on scale estimator to handle the influence of outlier values. In order to get new estimators, the root mean square error (RMSE) criterion is used to check the efficiency between the new technique and the classical method. The research showed that the new technique (M-estimation based on scale estimator) yields more accurate parameter estimates than the traditional approach (OLS) in all simulated cases.