Absolute Deviation Method Research Articles

Comparison of noise estimation methods used in denoising 99mTc-sestamibi parathyroid images using wavelet transform.

The objective of this study was to compare the performance of variance, median absolute deviation, and the square of median absolute deviation methods of noise estimation in denoising of 99mTc-sestamibi parathyroid images using wavelet transform. Sixty-eight 99mTc-sestamibi parathyroid images including 33 images acquired at zoom 1.0 and 35 acquired at zoom 2.0 were denoised using the wavethresh package in R. The image decomposition and reconstruction method discrete wavelet transform, wavelet filter db4, shrinkage method hard, and thresholding policy universal were used. The noise estimation in the process was made using var, mad and madmad functions, which use variance, mean absolute deviation, and the square of mean absolute deviation, respectively. The quality of denoised images was assessed both qualitatively and quantitatively. A nonparametric two-sample Kolmogorov–Smirnov test was applied to find whether the difference in image quality produced by these three noise estimation methods was significant at 95% confidence. Noise estimation using madmad function produced the best quality denoised image. Further, the quality of the denoised image using madmad function was significantly better than the quality of the denoised image obtained with var or mad function (P = 1). The estimation of noise using madmad functions in wavelet transforms provides the best-denoised image for both zoom 1.0 and zoom 2.0 99mTc-sestamibi parathyroid images.

Accuracy of impressions for multiple implants: A comparative study of digital and conventional techniques

Statement of problemIntraoral scanning has benefits over conventional impression making, but whether scanning is sufficiently accurate for multiple implants is unclear. PurposeThe purpose of this in vitro study was to compare the trueness of digital scans acquired by using intraoral scanners from a small range to a complete arch with the conventional impression technique and to determine the influence of 2 different evaluation methods (best-fit algorithm versus absolute linear deviation) on the outcomes of accuracy assessment. Material and methodsA mandibular model with 8 implants (A-H) around an edentulous arch was used as the master model. Open-format standard tessellation language (STL) data sets (1 reference file from a highly accurate dental laboratory scanner, 10 files from an intraoral scanner, and 10 files from digitized conventional impressions at room temperature) were imported to a metrology software program, and 5 groups of scanning ranges (AB, FGH, CDEF, BCDEFG, and ABCDEFGH) were identified simulating different clinical situations. Two evaluation methods—root mean square values calculated from the best-fit algorithm and average value of linear discrepancies from absolute linear deviation—were used to describe the trueness values. The impacts of different scanning or impression methods, ranges, and evaluation methods were tested by using a 3-way ANOVA. The effect of the scanning range on accuracy was further identified with 1-way ANOVA. The paired-sample t test was used to determine the differences of trueness values between the 2 methods in different groups. ResultsThe trueness values of the implant impressions were significantly affected by different scanning or impression methods (P<.001), evaluation methods (P<.001), and scanning ranges (P<.001) as independent variables. With use of the best-fit algorithm, deviations from the digital scans were significantly greater than those from the conventional impressions in cross-arch situations (groups CDEF, BCDEFG, and ABCDEFGH). With use of the absolute linear deviation method, statistically significant lower accuracy was found when larger areas were encountered (groups BCDEFG and ABCDEFGH). Use of the absolute linear deviation method resulted in a higher mean score of inaccuracy than that from the best-fit algorithm method in most situations. ConclusionsScanning or impression methods, ranges, and evaluation methods affected the dimensional accuracy (trueness) of scans or impressions with multiple implants. Digital scans had worse trueness values compared with those made with the conventional splinting open-tray technique when cross-arch implant impressions were acquired.

A novel perspective for parameter estimation of seemingly unrelated nonlinear regression

Nonlinear regression is commonly used as a modeling tool to get a functional form between inputs and response variables when the inputs and the responses have a nonlinear relationship. It should be better to compose the predicted nonlinear models with considering correlation between the responses for multi-response data sets. For this purpose, seemingly unrelated nonlinear regression (SUNR) have been widely used in the literature. The parameter estimation procedure of the SUNR is based on nonlinear least squares (NLS) method, based on L 2-norm. However, it is possible to use different norms for parameter estimation process. The novelty of this study is presenting the applicability of least absolute deviation (LAD) method, defined in L 1-norm, with the NLS method simultaneously for obtaining parameter estimates of the SUNR model in a multi objective perspective. In this study, the proposed multi-objective SUNR model is called MO-SUNR. The optimization of the MO-SUNR model is achieved by using soft computing methods. Two data set examples are given for application purposes of the MO-SUNR model. It is seen from the results that the MO-SUNR provides many alternatively usable compromise parameter estimates through the simultaneous evaluation of the LAD and the NLS methods.

Application of Object-Oriented Median Absolute Deviation Method to Building Extraction

Application of Object-Oriented Median Absolute Deviation Method to Building Extraction

Robust Estimation Based on the Least Absolute Deviations Method and the Kalman Filter

We propose a new approach for solving the filtering problem in linear systems based on incomplete measurements, where the characteristics of the dynamic noise are not known exactly, and measurements may contain anomalous non-Gaussian errors. The proposed algorithm is based on the idea of using the adaptive Kalman filter and the generalized least absolute deviations method jointly. With numerical modeling, we show that, compared to the classical optimal linear filtering method, our solution has lower sensitivity to short-term outliers in measurements and provides a quick adjustment of the parameters of the system dynamics. The proposed algorithm can be used to solve onboard navigation and tracking problems on aircrafts. To implement the method of least absolute deviations, we use an efficient L1-optimization algorithm.

Ellipsoid Method for Linear Regression Parameters Determination

Introduction. Linear regression parameters determination can be formulated as a non-smooth function minimization problem, which is Lp-norm of residual of the linear equations system. To solve it non-smooth function minimization methods can be used, e.g. subgradient methods. The article [7] considers ellipsoid method application for finding Lp-solution of redefined linear equations system with 1≤p≤2. The purpose of the paper is to extend the algorithm based on the ellipsoid method for a linear regression parameters determination problem with an arbitrary value of parameter p≥2 so that under big values of p the solution of the problem equals minimax method solution, which corresponds to p=∞ case. To describe the formulation of observation approximation problem with quadratic function as linear regression parameters determination problem. To analyze algorithm work results for great number of observations and outliers. To compare the minimax method and the ellipsoid method algorithm work results for linear regression parameters determination problem with big values of parameter p. Results. The way of calculation of objective function and its subgradient values with large values of parameter p was developed and verified on example of observation approximation containing outliers with linear function. Algorithm based on ellipsoid method changes linear function parameters monotonically using parameter p adjusting, thereby permits to reject or consider these or those observations. It is shown in [3] that Least Absolute Deviations method (LAD) is advised to be used as far as it ignores outliers and reconstructs linear function accurately. Experiment results with big number of observations and outliers using p=1 confirmed that conclusion: LAD ignores outlier groups and approximates observations with linear function adequately. Least Square Method (LSM) deviates from optimal linear function if a group of outliers is present in particular area. In case of using big values of parameter p problem solution converges to minimax method solution. Conclusions. Algorithm based on ellipsoid method permits to determine linear regression parameters with arbitrary value of parameter p≥1. So, three known methods can be used – LAD, LSM and minimax method – as its special cases. Moreover, directing p to 1, intensity of outliers ignoring can be regulated, that gives a possibility to use external sources of information (expert opinions, measuring devices readings, statistical forecasts, etc.) for more correct and adequate approximation function reconstruction. Keywords: ellipsoid method, linear regression, outliers.

Impact of herding on the returns in the Indian stock market: an empirical study

PurposeHerd behavior has been studied herein and tested based on primary respondents from Indian markets.Design/methodology/approachThe paper expounds the empirical evidence by applying the cross-sectional absolute deviation method and reporting on herd behavior among decision-makers who are engaged in trading in the Indian stock market. Further, the study attempts to analyze the market-wide herding in the Indian stock market using 2230 daily, 470 weekly and 108 monthly observations of Nifty 50 stock returns for a period of nine years from April 1, 2009 to March 31, 2018 during the normal market conditions, extreme market conditions and in both increasing and decreasing market conditions.FindingsIn a span of a decade witnessing different market cycles, the authors’ results exhibit that there is no evidence of herding in any market condition in Indian stock market primarily due to the dominance of institutional investors and secondly because of low market participation by individual investors.Originality/valueThe results reveal that there is no impact of herd behavior on the stock returns in the Indian equity market during the normal market conditions. It highlights that the participation of individuals who are more prone to herding is more evident for short-run investments, contrary to long-term holdings.

Robust Identification of an Exponential Autoregressive Model

One of the most common nonlinear time series (random processes with discrete time) models is the exponential autoregressive model. In particular, it describes such nonlinear effects as limit cycles, resonant jumps, and dependence of the oscillation frequency on amplitude. When identifying this model, the problem arises of estimating its parameters --- the coefficients of the corresponding autoregressive equation. The most common methods for estimating the parameters of an exponential model are the least squares method and the least absolute deviation method. Both of these methods have a number of disadvantages, to eliminate which the paper proposes an estimation method based on the robust Huber approach. The obtained estimates occupy an intermediate position between the least squares and least absolute deviation estimates. It is assumed that the stochastic sequence is described by the autoregressive equation of the first order, is stationary and ergodic, and the probability distribution of the innovations process of the model is unknown. Unbiased, consistency and asymptotic normality of the proposed estimate are established by computer simulation. Its asymptotic variance was found, which allows to obtain an explicit expression for the relative efficiency of the proposed estimate with respect to the least squares estimate and the least absolute deviation estimate and to calculate this efficiency for the most widespread probability distributions of the innovations sequence of the equation of the autoregressive model

HERD BEHAVIOR IN TERMS OF SOCIAL PSYCHOLOGY: THE EXAMPLE OF CRYPTO ASSET MARKETS

The four main causes of sudden price fluctuations in crypto asset markets are speculative news within the scope of global developments, positive market perceptions that increase the price, rising stock market values, and herd behavioral tendencies on crypto assets. The purpose of this study is to examine herd behavior trends in terms of social psychology, exemplified by the crypto asset market. In this framework, firstly, the daily yield pricing of twenty-two crypto assets with the highest transaction volume was examined in the period of August 2013 - December 2019, and then the trend of herd behavior of these assets on the market was investigated. Cross Sectional Absolute Deviation (CSAD) method was used to measure the herd behavior trend. The analysis revealed the presence of herd behavior trend in regime 1 and regime 3 models.

MAD Control Chart for Autoregressive Models with Skew-Normal Distribution

AbstractThe major problem in analyzing control charts is to work with autocorrelated data. This problem can be solved by fitting a suitable model to the data and using the control chart for the residuals. The problem becomes very important, when the distribution of observation is nonnormal, in addition to being autocorrelated. Much recent research has focused on the development of appropriate statistical process control techniques for the autocorrelated data or nonnormal distribution, but few studies have considered monitoring the process mean of both nonnormal and autocorrelated data. In this paper, a simulation study is conducted to compare the performances of the control chart based on the median absolute deviation method (MAD) with those of existing control charts for the skew normal distribution. Simulation results indicate considerable improvement over existing control charts for nonnormal data can be achieved when the control charts with control limits based on the MAD method are used to monitor the process mean of nonnormal autocorrelated data.

Application of group smoothly clipped absolute deviation method in identifying correlates of psychiatric distress among college students

BackgroundCollege students are at an increased risk of psychiatric distress. So, identifying its important correlates using more reliable statistical models, instead of inefficient traditional variable selection methods like stepwise regression, is of great importance. The objective of this study was to investigate correlates of psychiatric distress among college students in Iran; using group smoothly clipped absolute deviation method (SCAD).MethodsA number of 1259 voluntary college students participated in this cross-sectional study (Jan-May 2016) at Hamadan University of Medical Sciences, Iran. The data were collected using a self-administered questionnaire consisting of demographic information, a behavioral risk factors checklist and the GHQ-28 questionnaire (with a cut-off of 23 to measure psychiatric distress, recommended by the Iranian version of the questionnaire). Penalized logistic regression with a group-SCAD regularization method was used to analyze the data (α = 0.05).ResultsThe majority of students were aged 18–25 (87.61%), and 60.76% of them were female. About 41% of students had psychiatric distress. Significant correlates of psychiatric distress among college students selected by group-SCAD included the average grade, educational level, being optimistic about future, having a boy/girlfriend, having an emotional breakup, the average daily number of cigarettes, substance abusing during previous month and having suicidal thoughts ever (P < 0.05).ConclusionsPenalized logistic regression methods such as group-SCAD and group-Adaptive-LASSO should be considered as plausible alternatives to stepwise regression for identifying correlates of a binary response. Several behavioral variables were associated with psychological distress which highlights the necessity of designing multiple factors and behavioral changes in interventional programs.

Composite quasi-likelihood for single-index models with massive datasets

The single-index models (SIMs) provide an efficient way of coping with high-dimensional nonparametric estimation problems and avoid the “curse of dimensionality.” Many existing estimation procedures for SIMs were built on least square loss, which is popular for its mathematical beauty but is non-robust to non-normal errors and outliers. This article addressed the question of both robustness and efficiency of estimation methods based on a new data-driven weighted linear combination of convex loss functions instead of only quadratic loss for SIMs. The optimal weights can be chosen to provide maximum efficiency and these optimal weights can be estimated from data. As a specific example, we introduce a robust method of composite least square and least absolute deviation methods. Moreover, we extend the proposed method to the analysis of massive datasets via a divide-and-conquer strategy. The proposed approach significantly reduces the required primary memory and the resulting estimate is as efficient as if the entire dataset was analyzed simultaneously. The asymptotic normality of the proposed estimators is established. The simulation studies and real data applications are conducted to illustrate the finite sample performance of the proposed methods.

Temporal stability of soil water content on slope during the rainy season in gully regulation watershed

To increase reserve farmland resources, ensure food security, and to improve the ecological environment, large-scale gully regulation projects in Yan'an area of the Loess Plateau have been undertaken in China since 2013. Understanding the spatial and temporal distribution characteristics of slope soil moisture during the rainy season is, therefore, important for vegetation selection used in restoration measures in this area. The temporal stability of soil water content in the upper 100 cm soil depth in a gully regulation watershed was examined using six measurements taken during the rainy season (May–November 2016). Temporal stability analysis of the soil water content was undertaken using Spearman’s rank correlation coefficient and the relative difference method. Results showed that soil water content in the 0–50 cm and 0–100 cm soil depths demonstrated moderate temporal and spatial variability, and soil water content variability gradually decreased with increasing soil depth. Soil water content in both depths had strong temporal stability, having a positive correlation with soil depth. Furthermore, temporal stability may decrease with increasing soil water content (p < 0.05). The number and position of representative locations were not constant, which varies with the soil depth and the estimation method. Five methods (mean relative difference, minimum relative difference standard equilibrium, temporal stability index, mean absolute deviation and root mean square error) can be used to determine the representative location of temporal stability. However, the prediction accuracy of the soil water content at representative locations obtained using the different methods for mean slope water content differed. Comparatively, the mean absolute deviation method had a higher accuracy, followed by the minimum relative difference standard equilibrium method. Results indicate that the prediction accuracy of five methods increased with soil depth.

Water chemistry and estimation of background levels of elements in surface water bodies from a protected area in the vicinity of Fe deposits, Southeastern Amazon

This paper aimed to provide the knowledge of water chemistry and quality and background values of the elements in surface waters around the Fe-ore deposits of N3 and N4WSul of Serra dos Carajás, Brazil. Water samples were collected from 17 monitoring points monthly/quarterly between 2013 and 2016 and the elemental analyses were carried out using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The waters are slightly acidic to slightly alkaline in nature and are classified into “good” and “optimum” water quality category. Seasonal variation impacted water quality, with significantly higher content of metals (such as Fed, Fet, and Ald) during the rainy period that is due to more intense surface-runoff and leaching. Comparing with CONAMA 357/05/WHO limits, nonconformities were mainly observed for pH, Fe and Mn, and in some cases for Ald, Znt, Cud, Cdt and Znt, and were significantly higher in the N4WSul area than in the N3. Although, Fe and Mn mostly enter the water bodies from catchment weathering, the poor correlation between Fe and Mn indicated that their source and transport processes are different. In fact, elevated Mnd concentrations associated with low DO content in the dry period are explained by a reductive dissolution of manganese oxides via bacterial decomposition of OM. The background threshold values (BTVs) of elements were estimated by the upper 95% simultaneous limit (USL95; using ProUCL software) and the median ± 2 Median Absolute Deviation (mMAD) method, while the former was considered as the most suitable method for defining BTVs. The BTVs for Al(d), Fe (d), Mn and Zn are mostly exceeding the maximum limits stipulated by the legislation. However, since these areas are located in a protected region, these values are seen as representative of the natural conditions of the study area and reflect geogenic influence.

Equivalence assessment and leveling of geochemical datasets to generate integrated geochemical maps: Application to mineral exploration

Merging geochemical datasets of a few adjacent areas (areas with uniform geology) originated from two or more independent geochemical surveys can be highly beneficial to generate the integrated geochemical maps. To merge two or more independent datasets that are somehow linked together and create a new combined dataset, the significance of the shift between these datasets must be determined. Method of sampling, sample preparation, and chemical analysis are among the factors that critically impact on the measurement of the element concentration, thus explaining the inconsistency in the concentration of the same elements in various geochemical surveys. Therefore, studying the presence of a shift between datasets and leveling them as a pre-processing stage are necessary to combine geochemical datasets of adjacent areas and generate the integrated geochemical map. The purpose of assessing the equivalence is to recognize if there is a significant shift between values of the same variables (elements in geochemical surveys) in several datasets. The assessment is done by different methods, such as Quantile-Quantile Dispersion (QQD) diagram and a visual method dependent on expert's judgment to be introduced in this study. In this study, the Fisher test, T-student test, and Quantile-Value Diagram (QVD), a valid method showing the least shift between multiple datasets, were applied for the equivalence assessment between two or more datasets. Fisher and T-student tests were run to compare the variances and means of the same elements in two datasets from adjacent areas. The aim of the study was to generate an integrated geochemical map out of two independent geochemical surveys, in the north of Sarduiyeh and south of Rayen geological sheets with uniform geology (southern part of Urmia–Dokhtar metallogenic belt of Iran), to identify new anomalous areas. The equivalence assessment was performed for twelve elements, including Zn, Pb, Ag, Ni, Bi, Cu, As, Sb, Co, W, MO, and Mn. Based on QQD diagram method, some elements of the two databases such as Pb, Ni, and Bi were considered as equivalent, yet the QVD method showed a significant shift for them. Fisher and T-student tests confirmed the results of QVD. All the chemical elements of this survey exhibited shift in the adjacent maps and therefore required to be adjusted. For so doing, QQD diagram was drawn based on quantile pairs of shifted and reference datasets. A linear regression equation fitted in quantile pairs determined the relationship between the shifted and reference datasets. To adjust, it is important to select the reference dataset, which is the one with the best analysis method. Yet, it is difficult to select the reference dataset when the analysis method of the two geochemical surveys is the same. To timely address the problem, one of the two datasets was selected as the reference and an integrated geochemical map was separately generated for each element. It is deduced that if the dataset with larger quantile values is selected as the reference, more anomalies are shown, and vise verse. This approach, however, is based on the expert's judgment and may lead to unreal and unusual results. In another approach, which is more objective and cost-effective, the dataset, the median of which is close to the average of continental crust or the regional background if available, is used as the reference. This led to just one integrated geochemical map. Median Absolute Deviation (MAD) method was then applied to calculate the threshold of elements in integrated geochemical maps.

General Fitting Methods Based on Lq Norms and their Optimization

The widely used fitting method of least squares is neither unique nor does it provide the most accurate results. Other fitting methods exist which differ on the metric norm can be used for expressing the total deviations between the given data and the fitted statistical model. The least square method is based on the Euclidean norm L2, while the alternative least absolute deviations method is based on the Taxicab norm, L1. In general, there is an infinite number of fitting methods based on metric spaces induced by Lq norms. The most accurate, and thus optimal method, is the one with the (i) highest sensitivity, given by the curvature at the minimum of total deviations, (ii) the smallest errors of the fitting parameters, (iii) best goodness of fitting. The first two cases concern fitting methods where the given curve functions or datasets do not have any errors, while the third case deals with fitting methods where the given data are assigned with errors.

Outliers and misleading leverage effect in asymmetric GARCH-type models

Abstract This paper illustrates how outliers can affect both the estimation and testing of leverage effect by focusing on the TGARCH model. Three estimation methods are compared through Monte Carlo experiments: Gaussian Quasi-Maximum Likelihood, Quasi-Maximum Likelihood based on the Student-t likelihood and Least Absolute Deviation method. The empirical behavior of the t-ratio and the Likelihood Ratio tests for the significance of the leverage parameter is also analyzed. Our results put forward the unreliability of Gaussian Quasi-Maximum Likelihood methods in the presence of outliers. In particular, we show that one isolated outlier could hide true leverage effect whereas two consecutive outliers bias the estimated leverage coefficient in a direction that crucially depends on the sign of the first outlier and could lead to wrongly reject the null of no leverage effect or to estimate asymmetries of the wrong sign. By contrast, we highlight the good performance of the robust estimators in the presence of one isolated outlier. However, when there are patches of outliers, our findings suggest that the sizes and powers of the tests as well as the estimated parameters based on robust methods may still be distorted in some cases. We illustrate these results with two series of daily returns.

Kinematic and Dynamic Vehicle Model-Assisted Global Positioning Method for Autonomous Vehicles with Low-Cost GPS/Camera/In-Vehicle Sensors.

Real-time, precise and low-cost vehicular positioning systems associated with global continuous coordinates are needed for path planning and motion control in autonomous vehicles. However, existing positioning systems do not perform well in urban canyons, tunnels and indoor parking lots. To address this issue, this paper proposes a multi-sensor positioning system that combines a global positioning system (GPS), a camera and in-vehicle sensors assisted by kinematic and dynamic vehicle models. First, the system eliminates image blurring and removes false feature correspondences to ensure the local accuracy and stability of the visual simultaneous localisation and mapping (SLAM) algorithm. Next, the global GPS coordinates are transferred to a local coordinate system that is consistent with the visual SLAM process, and the GPS and visual SLAM tracks are calibrated with the improved weighted iterative closest point and least absolute deviation methods. Finally, an inverse coordinate system conversion is conducted to obtain the position in the global coordinate system. To improve the positioning accuracy, information from the in-vehicle sensors is fused with the interacting multiple-model extended Kalman filter based on kinematic and dynamic vehicle models. The developed algorithm was verified via intensive simulations and evaluated through experiments using KITTI benchmarks (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and data captured using our autonomous vehicle platform. The results show that the proposed positioning system improves the accuracy and reliability of positioning in environments in which the Global Navigation Satellite System is not available. The developed system is suitable for the positioning and navigation of autonomous vehicles.

Anomaly delineation of porphyry copper deposits of Hanza Region through geochemical data analyses and multispectral remote sensing

Hanza region is located in the southern part of Urumieh–Dokhtar Metallogenic belt in southeastern Iran. This region includes six known porphyry copper deposits and it is considered as an ore- bearing region from geochemical point of view. The aim of this research is to examine effective processing techniques in the analysis of stream sediment geochemical datasets and ASTER satellite images. The processing methods have led to identification of eight new prospective areas. These methods are aimed at providing univariate geochemical maps. The stream sediment geochemical mapping for Cu and Mo was performed by the sample catchment basin approach. The results derived from this approach have been mapped in four classes associated with the first quartile, third quartile and threshold values obtained from Median Absolute Deviation method. False-colour composite and band ratio techniques were adopted as two well-known methods for the processing of an ASTER scene spanning the study area. Eight new targets for possible mineralization have been resulted from geochemical data analyses. Image processing techniques on the ASTER multispectral data have also revealed widespread hydrothermal alterations associated with the known porphyry copper deposits and the new prospects.

Spatial distribution and trace element geochemistry of laterites in Kunche area: Implication for gold exploration targets in NW, Ghana

Kunche area is located in NW Ghana within the Wa-Lawra greenstone Birimian belt and is composed mainly of volcaniclastics, metasediments and some intrusive granitoids. The area is covered with laterites making it difficult to identify exploration targets. In this study, we describe a new methodology based on statistical tools and trace element geochemistry to map the pathfinder elements of gold deposits in lateritic context. However, the results of this study are specific to a particular context and cannot be extrapolated without supplementary studies to all the lateritic areas. In this respect, a total of 67 lateritic samples were collected from residually weathered materials and their spatial distribution was determined by means of the GIS-based kriging interpolation method. The samples collected vary from detrital to residual laterites/duricrusts and are hosted in volcaniclastic rocks. ICP-MS and XRF techniques were used to determine the element concentrations of the samples. The trace element geochemical data were analyzed using bivariate and multivariate geostatistical analysis to establish relationships among elements. Fe-oxides such as goethite and hematite and clay minerals like kaolinite are the main secondary minerals of the concretionary reddish lateritic samples. All the analyzed elements showed asymmetrical distribution rather than normal distribution. Spearman correlation shows that Cu, Pb, S, As, and Ag have moderate to strong positive correlation with Au. From the multivariate geostatistical analysis, three element associations; a) Fe, Pb, S, Co, Cr; b) Ni, Y, Rb, Sr, Zn, and c) Ca, Cu, Mn, Ti, Zr, As, Au, Ag were observed. Threshold values of selected elements were determined using the median absolute deviation (MAD) method, which indicates possible anomalous concentrations in the laterites for Pb (≥48 ppm), Cu (≥46 ppm), As (≥134.2 ppm), and Ag (≥0.42 ppm). Multi-element mapping indicates that Pb + Cu + As + Ag is the most ideal association in the exploration of gold deposits. It reveals ellipsoidal anomalies comparable to the Au distribution map that suggest dispersion and accumulation of the pathfinder elements in the area. The geochemical anomalies are mainly restricted to the environment of the residual laterites in the Kunche area and we recommend that exploration programs should be focused in such areas.