Maximum Likelihood Estimation Research Articles

Optimality of multisensory integration while compensating for uncertain visual target information with artificial vibrotactile cues during reach planning

BackgroundPlanning and executing movements requires the integration of different sensory modalities, such as vision and proprioception. However, neurological diseases like stroke can lead to full or partial loss of proprioception, resulting in impaired movements. Recent advances focused on providing additional sensory feedback to patients to compensate for the sensory loss, proving vibrotactile stimulation to be a viable option as it is inexpensive and easy to implement. Here, we test how such vibrotactile information can be integrated with visual signals to estimate the spatial location of a reach target.MethodsWe used a center-out reach paradigm with 31 healthy human participants to investigate how artificial vibrotactile stimulation can be integrated with visual-spatial cues indicating target location. Specifically, we provided multisite vibrotactile stimulation to the moving dominant arm using eccentric rotating mass (ERM) motors. As the integration of inputs across multiple sensory modalities becomes especially relevant when one of them is uncertain, we additionally modulated the reliability of visual cues. We then compared the weighing of vibrotactile and visual inputs as a function of visual uncertainty to predictions from the maximum likelihood estimation (MLE) framework to decide if participants achieve quasi-optimal integration.ResultsOur results show that participants could estimate target locations based on vibrotactile instructions. After short training, combined visual and vibrotactile cues led to higher hit rates and reduced reach errors when visual cues were uncertain. Additionally, we observed lower reaction times in trials with low visual uncertainty when vibrotactile stimulation was present. Using MLE predictions, we found that integration of vibrotactile and visual cues followed optimal integration when vibrotactile cues required the detection of one or two active motors. However, if estimating the location of a target required discriminating the intensities of two cues, integration violated MLE predictions.ConclusionWe conclude that participants can quickly learn to integrate visual and artificial vibrotactile information. Therefore, using additional vibrotactile stimulation may serve as a promising way to improve rehabilitation or the control of prosthetic devices by patients suffering loss of proprioception.

Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test).

The selection of auxiliary variables is an important first step in appropriately implementing missing data methods such as full information maximum likelihood (FIML) estimation or multiple imputation. However, practical guidelines and statistical tests for selecting useful auxiliary variables are somewhat lacking, leading to potentially biased estimates. We propose the use of random forest analysis and lasso regression as alternative methods to select auxiliary variables, particularly in situations in which the missing data pattern is nonlinear or otherwise complex (i.e., interactive relationships between variables and missingness). Monte Carlo simulations demonstrate the effectiveness of random forest analysis and lasso regression compared to traditional methods (t-tests, Little's MCAR test, logistic regressions), in terms of both selecting auxiliary variables and the performance of said auxiliary variables when incorporated in an analysis with missing data. Both techniques outperformed traditional methods, providing a promising direction for improvement of practical methods for handling missing data in statistical analyses.

A New Approach Using the Lindley Distribution in Stochastic Frontier Analysis

Abstract Stochastic Frontier Analysis plays a crucial role in assessing technical efficiency and modelling production processes across various disciplines. Traditionally, SFA assumes specific error distributions, such as the normal distribution for random effects and the half-normal distribution for technical efficiency. However, the choice of error distributions can significantly impact model estimation and interpretation. This study proposes a novel approach by incorporating the Lindley distribution as a flexible error distribution in SFA, termed Lindley Stochastic Frontier Analysis (L-SFA). This extension offers a more detailed representation of the error structure, potentially enhancing the accuracy of efficiency estimates. The derivation and solution of maximum likelihood estimators for the theoretical foundations of L-SFA are provided. Furthermore, a simulation study demonstrates the advantages of L-SFA over traditional SFA. The findings underscore the importance of flexible error distributions in capturing the complexities of production processes with this new SFA extension. Keywords: Efficiency, Error distribution, Lindley stochastic frontier analysis, Maximum likelihood estimation, Simulation

Lactated Ringer vs Normal Saline Solution During Sickle Cell Vaso-Occlusive Episodes

Sickle cell disease (SCD), a clinically heterogenous genetic hemoglobinopathy, is characterized by painful vaso-occlusive episodes (VOEs) that can require hospitalization. Patients admitted with VOEs are often initially resuscitated with normal saline (NS) to improve concurrent hypovolemia, despite preclinical evidence that NS may promote erythrocyte sickling. The comparative effectiveness of alternative volume-expanding fluids (eg, lactated Ringer [LR]) for resuscitation during VOEs is unclear. To compare the effectiveness of LR to NS fluid resuscitation in patients with SCD and VOEs. This multicenter cohort study and target trial emulation included inpatient adults with SCD VOEs who received either LR or NS on hospital day 1. The Premier PINC AI database (2016-2022), a multicenter clinical database including approximately 25% of US hospitalizations was used. The analysis took place between October 6, 2023, and June 20, 2024. Receipt of LR (intervention) or NS (control) on hospital day 1. The primary outcome was hospital-free days (HFDs) by day 30. Targeted maximum likelihood estimation was used to calculate marginal effect estimates. Heterogeneity of treatment effect was explored in subgroups. A total of 55 574 patient encounters where LR (n = 3495) or NS (n = 52 079) was administered on hospital day 1 were included; the median (IQR) age was 30 (25-37) years. Patients who received LR had more HFDs compared with those who received NS (marginal mean difference, 0.4; 95% CI, 0.1-0.6 days). Patients who received LR also had shorter hospital lengths of stay (marginal mean difference, -0.4; 95% CI, -0.7 to -0.1 days) and lower risk of 30-day readmission (marginal risk difference, -5.8%; 95% CI, -9.8% to -1.8%). Differences in HFDs between LR and NS were heterogenous based on fluid volume received: among patients who received less than 2 L, there was no difference in LR vs NS; among those who received 2 or more L, LR was superior to NS. This cohort study found that, compared with NS, LR had a small but significant improvement in HFDs and secondary outcomes including 30-day readmission. These results suggest that, among patients with VOEs in whom clinicians plan to give volume resuscitation fluids on hospital admission, LR should be preferred over NS.

On Dagum-Pareto mixture distribution: properties, simulation and application in insurance

The most principal subject in actuarial science is the modeling of claims amount. There are two types of distributions associated with two types of risk. We speak of light-tailed distributions as opposed to heavy-tailed distributions or extreme value distribution. Among the distributions of the amount of heavy tail claim is: Weibull, Log-Normal, Pareto and Burr “Also named DAGUM”. Many attempts were implemented in expanding the classes of mixed and compound distributions. In this article, we present a new distribution by mixing Dagum distribution (α, λ, r) and Pareto distribution (α, λ) named Dagum-Pareto mixture distribution. Some of statistical properties of this new distribution are obtained which include reliability function, hazard function, skewness, kurtosis and quantile function. Also we use the maximum likelihood method to estimate the model parameters. A Simulation study was carried out to examine the performance and accuracy of the maximum likelihood estimates of the proposed distribution. An application of the new model to no-life insurance real data set is presented to illustrate its usefulness and applicability. In our case, it is proven that Dagum-Pareto mixture distribution provides better fit compared to others existing probability distributions using some goodness of fit measures.

Estimation and Analysis of Trigonometric Models under Bayesian Approach

In this study, we explore three innovative trigonometric models within the Bayesian framework, utilizing the inverse Weibull distribution as our foundation. These models—namely the Sine inverse Weibull, Cosine inverse Weibull, and Tan inverse Weibull—are crafted from distinct distribution families. We employ both maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) simulation techniques to estimate parameters, drawing upon a comprehensive dataset. By scrutinizing posterior samples numerically and graphically, we evaluate the efficacy of our models, generating Bayes estimates for parameters, examining reliability and hazard functions, and establishing credible intervals. Furthermore, we assess the predictive capacity of all three models through posterior predictive checks. We also conduct comparative analyses, pitting our models against competing ones using real-world data. Notably, our results reveal that the proposed trio of models exhibit strikingly similar performance in terms of fitting the data.

On the characterization of reflective surfaces using dual-polarization GNSS-R

Global navigation satellite systems reflectometry (GNSS-R) is a technique to extract information from reflecting surfaces by the reflected GNSS signals. GNSS-R has garnered increasing attention in the scientific literature due to its continuous global coverage and its superior spatial resolution. Moreover, operating in the L-band renders GNSS-R relatively immune to adverse weather conditions and affords high sensitivity to soil electrical properties. This work introduces a new approach with a dual-polarization antenna, left-hand circular polarized (LHCP) and right-hand circular polarized (RHCP), receiving the reflected signal from a sufficiently smooth surface so that all reflected energy arrives from the specular reflection point. The objective is to characterize the reflecting surface by extracting the relative permittivity and conductivity from the reflected signal. In contrast to other studies found in the literature, the reflection of the GNSS signal on different materials, including dielectric and conductive materials is considered. We derive a maximum likelihood estimator (MLE) for estimating the dielectric parameters of the reflective surface and other parameters of the reflected signal. We also derive the respective Cramer–Rao Lower Bound (CRLB) evaluating the performance of the MLE. The attained results are assessed based on the signal-to-noise ratio (SNR) and the angle of reflection of the reflected signal, which are the parameters that predominantly influence the proposed approach. Lower elevation angles, for instance, lead to higher estimation accuracy, while for reflective surfaces composed of metallic materials a higher SNR is needed to yield favorable estimation performance. Regarding dielectric materials, the estimation results are encouraging and thus enable diverse remote sensing applications by GNSS-R using the proposed setup.

Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling.

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.

Comparison of different estimation methods for the inverse power perk distribution with applications in engineering and actuarial sciences

Introducing the Inverse Power Perk distribution, this paper presents a versatile probability distribution designed to model positively skewed data with unprecedented flexibility. Building upon the Perk distribution, it accommodates a wide range of shapes including right-skewed, J-shaped, reversed J-shaped, and nearly symmetric densities, as well as hazard rates exhibiting various patterns of increase and decrease. The paper delves into the mathematical properties of this novel distribution and offers a comprehensive overview of estimation techniques, including maximum likelihood estimators, ordinary least square estimators, percentile-based estimators, maximum product of spacing estimators, Cramer-von Mises, weighted least squares estimators, and Anderson-Darling estimators. To assess the performance of these estimation methods across different sample sizes, Monte Carlo simulations are conducted. Through comparisons of average absolute error and mean squared error, the efficacy of each estimator is evaluated, shedding light on their suitability for both small and large samples. In a practical application, three real datasets, including insurance data, are employed to demonstrate the versatility of the current model, when comparing to existing alternatives. The IPP distribution offers significant advantages over traditional distributions, particularly in its superior ability to model tail risks, making it an invaluable tool for practitioners dealing with extreme values and rare events. Its computational efficiency further sets it apart, enabling more robust and faster analysis in large-scale datasets.This empirical analysis further underscores the utility and adaptability of the Inverse Power Perk model in capturing the nuances of diverse datasets, thereby offering valuable insights for practitioners in various fields.

Selection with two alleles of X-linkage and its application to the fitness component analysis of OdsH in Drosophila.

In organisms with the XY sex-determination system, there is an imbalance in the inheritance and transmission of the X chromosome between males and females. Unlike an autosomal allele, an X-linked recessive allele in a female will have phenotypic effects on its male counterpart. Thus, genes located on the X chromosome are of particular interest to researchers in molecular evolution and genetics. Here we present a model for selection with two alleles of X-linkage to understand fitness components associated with genes on the X chromosome. We apply this model to the fitness analysis of an X-linked gene, OdsH (16D), in the fruit fly Drosophila melanogaster. The function of OdsH is involved in sperm production and the gene is rapidly evolving under positive selection. Using site-directed gene targeting, we generated functional and defective OdsH variants tagged with the eye-color marker gene white. We compare the allele frequency changes of the two OdsH variants, each directly competing against a wild-type OdsH allele in concurrent but separate experimental populations. After 20 generations, the two genetically modified OdsH variants displayed a 40% difference in allele frequencies, with the functional OdsH variant demonstrating an advantage over the defective variant. Using maximum likelihood estimation, we determined the fitness components associated with the OdsH alleles in males and females. Our analysis revealed functional aspects of the fitness determinants associated with OdsH, and that sex-specific fertility and viability consequences both contribute to selection on an X-linked gene.

Interpersonal Violence in Five Regions in Asia: Ecological Risk Factors Associated with Perceptions of Justifiability of Violence.

In Asia, rates of interpersonal violence are increasing, with significant regional disparities. However, long-term, continental-scale research considering regional differences across the Asia regions is limited. Guided by the ecological model, we examined five ecological risk factors (low life satisfaction/happiness, economic hardship, neighborhood disadvantage, patriarchal values, and religiosity) associated with perceptions of justification of interpersonal violence (i.e., intimate partner violence [IPV] against wife, child physical abuse, and violence against others) in five regions in Asia (i.e., East, West, Central, South, and Southeast). Using the World Values Survey (n = 32,307), a multigroup multiple regression model was used with robust maximum likelihood estimation using Mplus ver. 8. In the entire Asia sample model, perceptions of justifiability of IPV against wife were positively associated with low life satisfaction/happiness; economic hardship; neighborhood disadvantage; and patriarchal values, while they were negatively associated with religiosity. Perceptions of justifiability of child abuse were positively associated with low life satisfaction/happiness; neighborhood disadvantage; and patriarchal values, while they were negatively associated with economic hardship and religiosity. Perceptions of justifiability of violence against others were positively associated with economic hardship and neighborhood disadvantage, while they were negatively associated with religiosity. Each region presented unique risk factor associations. Considering the high rates of interpersonal violence in Asia, understanding the risk factors associated with perceptions of justifying specific types of interpersonal violence can provide an initial insight into preventing violence in Asia. Further, as many Asians dwelling outside Asian regions are still influenced by their culture, religion, language, and norms of the region of origin, the study findings may shed light on future studies to consider in the interpersonal violence literature.

Modelling the age distribution of longevity leaders

Human longevity leaders with remarkably long lifespan play a crucial role in the advancement of longevity research. In this paper, we propose a stochastic model to describe the evolution of the age of the oldest person in the world by a Markov process, in which we assume that the births of the individuals follow a Poisson process with increasing intensity, lifespans of individuals are independent and can be characterized by a gamma–Gompertz distribution with time-dependent parameters. We utilize a dataset of the world’s oldest person title holders since 1955, and we compute the maximum likelihood estimate for the parameters iteratively by numerical integration. Based on our preliminary estimates, the model provides a good fit to the data and shows that the age of the oldest person alive increases over time in the future. The estimated parameters enable us to describe the distribution of the age of the record holder process at a future time point.

Improved Liu-ridge-type estimates for the beta regression model

The main objective of this paper is to introduce a new class of estimators for the ill-conditioned beta regression model. The new class, named the Liu-ridge-type (LRT), incorporates two shrinkage parameters that allow it to include the maximum-likelihood estimator (MLE), the ridge estimator (RE), and the Liu estimator (LE) as special cases. The LRT estimator has been shown to outperform the LE, the RE, and the MLE in terms of the mean squared error (MSE) matrix under certain conditions. Simulated and real applications illustrate the potential benefits of the new LRT estimator.

Ab initio uncertainty quantification in scattering analysis of microscopy.

Estimating parameters from data is a fundamental problem in physics, customarily done by minimizing a loss function between a model and observed statistics. In scattering-based analysis, it is common to work in the reciprocal space. Researchers often employ their domain expertise to select a specific range of wave vectors for analysis, a choice that can vary depending on the specific case. We introduce another paradigm that defines a probabilistic generative model from the beginning of data processing and propagates the uncertainty for parameter estimation, termed the ab initio uncertainty quantification (AIUQ). As an illustrative example, we demonstrate this approach with differential dynamic microscopy (DDM) that extracts dynamical information through minimizing a loss function for the squared differences of the Fourier-transformed intensities, at a selected range of wave vectors. We first show that the conventional way of estimation in DDM is equivalent to fitting a temporal variogram in the reciprocal space using a latent factor model as the generative model. Then we derive the maximum marginal likelihood estimator, which optimally weighs the information at all wave vectors, therefore eliminating the need to select the range of wave vectors. Furthermore, we substantially reduce the computational cost of computing the likelihood function without approximation, by utilizing the generalized Schur algorithm for Toeplitz covariances. Simulated studies of a wide range of dynamical systems validate that the AIUQ method improves estimation accuracy and enables model selection with automated analysis. The utility of AIUQ is also demonstrated by three distinct sets of experiments: first in an isotropic Newtonian fluid, pushing limits of optically dense systems compared to multiple particle tracking; next in a system undergoing a sol-gel transition, automating the determination of gelling points and critical exponent; and lastly, in discerning anisotropic diffusive behavior of colloids in a liquid crystal. These studies demonstrate that the new approach does not require manually selecting the wave vector range and enables automated analysis.

Maximum likelihood estimators for colony-forming units.

Measuring the abundance of microbes in a sample is a common procedure with a long history, but best practices are not well-conserved across microbiological fields. Serial dilution methods are commonly used to dilute bacterial cultures to produce countable numbers of colonies, and from these counts, to infer bacterial concentrations measured in colony-forming units (CFUs). The most common methods to generate data for CFU point estimates involve plating bacteria on (or in) a solid growth medium and counting their resulting colonies or counting the number of tubes at a given dilution that have growth. Traditionally, these types of data have been analyzed separately using different analytic methods. Here, we build a direct correspondence between these approaches, which allows one to extend the use of the most probable number method from the liquid tubes experiments, for which it was developed, to the growth plates by viewing colony-sized patches of a plate as equivalent to individual tubes. We also discuss how to combine measurements taken at different dilutions, and we review several ways of analyzing colony counts, including the Poisson and truncated Poisson methods. We test all point estimate methods computationally using simulated data. For all methods, we discuss their relevant error bounds, assumptions, strengths, and weaknesses. We provide an online calculator for these estimators.Estimation of the number of microbes in a sample is an important problem with a long history. Yet common practices, such as combining results from different measurements, remain sub-optimal. We provide a comparison of methods for estimating abundance of microbes and detail a mapping between different methods, which allows to extend their range of applicability. This mapping enables higher precision estimates of colony-forming units (CFUs) using the same data already collected for traditional CFU estimation methods. Furthermore, we provide recommendations for how to combine measurements of colony counts taken across dilutions, correcting several misconceptions in the literature.

Estimation of Bonferroni Curve and Bonferroni Index of the Pareto Distribution

Abstract In this article, we consider classical and Bayesian estimations of some economic measures, specially Bonferroni Curve and Bonferroni Index of the Pareto distribution. We obtain the Maximum Likelihood Estimator and Uniform Minimum Variance Unbiased Estimator in the classical setup, and their properties are studied. Additionally, we conduct Bayesian estimation procedures based on symmetric loss functions and a truncated gamma prior. The precision of the estimators is evaluated under different sample sizes via Monte Carlo simulation. Furthermore, a real dataset is provided to compute all the estimators.

Assessment of reliability and availability of a system by using a bivariate stochastic model

AbstractIn the present age of industrial revolution where the smart manufacturing and smart production are being used in the developed countries, the production systems are still vulnerable to disruptions and outages that affect the reliability and availability of the systems. Certain scientific and probabilistic approaches are required to study the inter‐failure times and repair times and find the possible solutions for maximum production and minimum losses. In this article, we propose a bivariate Power Pareto distribution to model the inter‐failure times and repair times of a system along with some of its characteristics. The model parameters are estimated by employing the maximum likelihood estimation, Bayesian estimation and ant colony optimization algorithm. A simulation study is conducted for different sample sizes to assess the stability of the model parameters. Moreover, we derive the distribution of convolution from our proposed bivariate stochastic distribution and compute its quantiles that help to determine the total time of availability and recovery of a certain system. To demonstrate the efficacy of our model, we use real data set of inter‐failure times and repair times of a certain system.

Clinical and functional outcomes of locked plating vs. cerclage compression wiring for AO type C patellar fractures– a retrospective single-center cohort study

Abstract Purpose Although “tension-band wiring” is still commonly used to stabilize patellar fractures, the technique has recently been scrutinized due to biomechanical insufficiency. Consequently, the AO Foundation renamed the principle to compression cerclage wiring (CCW). Several studies propose favorable outcomes when utilizing locked plating (LP). This study aims to compare outcome of CCW and LP for complex patellar fractures. Methods A retrospective, single-center cohort study was performed on patients who underwent operative treatment for (AO 34 C-Type) patellar fractures between April 2013 and March 2023. Patients with a 12 month follow up were included. We grouped and compared patients based on the applied treatment strategy: group LP vs. group CCW. Primary outcome parameters included implant-related complications and revision surgeries. Secondary outcomes were length of stay, return to work and 12 months functional outcome (Lysholm score). Odd ratios for complications and revisions were calculated using the conditional Maximum Likelihood Estimate. The threshold for statistical significance was set at p < 0.05. Results Of 145 patients, 63 could be included (group LP: n = 23 and group CCW: n = 40). Fractures in group LP were significantly more complex in regard to AO Classification (p < 0.001), number of fragments (p < 0.001) and degree of comminution (p < 0.001), yet odds of complications were significantly lower in group LP (OR: 0.147; 95%CI: 0.015–0.742; p = 0.009). K-wire migration was the most common complication in group CCW (20%). Odds of revision surgery were significantly lower in group LP (OR: 0.000; 95%CI: 0.000-1.120; p = 0.041). The average Lysholm score at one year was favorable in both groups (89.8; SD: 11.9 in group LP and 90.6; SD: 9.3 in group CCW; n.s.). Conclusion In our study cohort, LP was routinely chosen for more complex fracture morphologies; nevertheless the data implies that LP may be considered as the superior fixation technique in regard to complications and revision operations. Especially, K-wire migration occurs frequently after CCW. The one year functional outcome was comparable between the groups, with both demonstrating good results. Prospective randomized studies are indicated to validate our findings.

Statistical Study of the Mechanical Behavior of the New Fiber from the Strelitzia Juncea Plant Fibers: Application in Ecological Yarns

ABSTRACT To develop efficient, durable, and eco-friendly natural fiber composites, this study examines the physical properties of Strelitzia Juncea (SJ) fiber for potential industrial use. The focus is on how gauge length affects mechanical properties of SJ fibers, aiming to manufacture textile yarns. Tensile tests were performed on SJ yarns of varying diameters (0.95, 1.26, and 1.76 mm). In addition, 150 fibers across five-gauge lengths (GL) from 10 to 50 mm gave a strength of 731 to 270 MPa and a Young’s modulus of 28 to 31 GPa, with statistical analysis applied to evaluate the data’s variability. Statistical techniques were applied to assess and quantify this dispersion. Results were analyzed using Weibull distributions with different probability indices, along with least squares (LS) and maximum likelihood (ML) estimation techniques. Furthermore, for each tensile strength characteristic, a one-factor analysis of variance (ANOVA) was performed across the five groups.

Performance Analysis of Filtered OFDM using MLE for Wireless Communication Networks

Introduction: OFDM has evolved as an effective modulation format for 4G mobile network technologies, Long-Term Evolution, and Worldwide Interoperability for Microwave Access. OFDM is the modulation technique adopted for communication standards such as DSL, wireless LAN, DVB, and DAB. However, because CP-OFDM has considerable out-of-band emission that may interfere with transmissions in adjacent bands and applies a single set of parameters to the whole band to fulfill a given service, it is unable to accommodate the diversity of services. The objective of the research is to develop a novel waveform with better adaptability than CP-OFDM that employs filters to increase spectrum containment. In this view, an FIR filter that uses the Blackman window function to lessen the OOBE is proposed in this manuscript. Filtered OFDM is sensitive to carrier frequency offset (CFO). CFO increases the Inter-Carrier Interference (ICI) in F-OFDM systems. However, research work that combines the CFO compensation technique with filtered OFDM has not been listed in the literature so far. Methods: In this paper, Maximum likelihood estimation is used to cancel the ICI caused by the CFO. The high PAPR of filtered OFDM is one of its drawbacks. The amplitude clipping method is used to reduce the PAPR. The distortion caused by amplitude clipping is prevented by filters used in the FOFDM system. Highlights: • A novel waveform addressing OOBE issues in OFDM is developed for 4G wireless communication networks. • An innovative solution using MLE is proposed to address the challenges faced by the filtered OFDM system due to the CFO. • Article addresses the high PAPR problem of filtered OFDM. Results: The performance of filtered OFDM with amplitude clipping is analyzed in terms of BER, PSD, and PAPR over the AWGN channel. Conclusion: Simulation results show that, when compared to the traditional OFDM system, such a combined method effectively suppresses PAPR while maintaining good BER (Bit Error Rate) and OOBE performance.