DSpace Repository :: Browsing by Author "Kumar, D"

Browsing by Author "Kumar, D"

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Alpha power transformed inverse Lindley distribution: A distribution with an upside-down bathtub-shaped hazard function
(2017-08) De, S; Nassar, M; Kumar, D
The inverse Lindley distribution has been generalized by many authors in recent years. Here, we introduce a new generalization called alpha power transformed inverse Lindley (APTIL) distribution that provides better fits than the inverse Lindley distribution and some of its known generalizations. The new model includes the inverse Lindley distribution as a special case. Various properties of the proposed distribution, including explicit expres sions for the mode, moments, conditional moments, mean residual lifetime, Bonferroni and Lorenz curves, entropies, stochastic ordering, stress–strength reliability and order statistics are derived. The new distribution can have an upside-down bathtub failure rate function depending on its parameters. The model parameters are obtained by the method of maximum likelihood estimation. The approximate confidence intervals of the model parameters are also obtained. A simulation study is carried out to examine the performance of the maximum likelihood estimators of the parameters. Finally, two data sets have been analyzed to show how the proposed model works in practice.
Cannabidiol-Loaded Nanoparticles Based on Crosslinked Starch: Anti-Inflammatory Activity and Improved Nose-to-Brain Delivery
(2023-06) Eydelman, I; N, Zehavi; Feinshtein, V; Kumar, D
Cannabidiol (CBD) has previously been shown to inhibit inflammatory cytokine production in both in vitro and in vivo studies of neurodegenerative diseases. To date, the CBD treatment of these diseases by quantitative targeting directly to the brain is one of the greatest challenges. In this paper, we present a new particulate system capable of delivering CBD into the brain via the intranasal route. Intranasal administration of CBD-loaded starch nanoparticles resulted in higher levels of cannabidiol in the brain compared to an identically administered cannabidiol solution. The production and the characterization of starch-based nanoparticles was reported, as well as the evaluation of their penetration and anti-inflammatory activity in cells. Cannabidiol-loaded starch nanoparticles were prepared by crosslinking with divanillin, using the nanoprecipitation method. Evaluation of the anti-inflammatory activity in vitro was performed using the BV2 microglia cell line. The starch nanoparticles appeared under electron microscopy in clusters sized approximately 200 nm in diameter. In cultures of lipopolysaccharide-induced inflamed BV2 cells, the cannabidiol-loaded starch nanoparticles demonstrated low toxicity while effectively reducing nitric oxide production and IL-6 levels. The anti-inflammatory effect was comparable to that of a glucocorticoid. Starch-based nanoparticle formulations combined with intranasal administration may provide a suitable platform for efficacious cannabidiol delivery and activity in the central nervous system.
Cannabidiol-Loaded Nanoparticles Based on Crosslinked Starch: Anti-Inflammatory Activity and Improved Nose-to-Brain Delivery
(2023-06) Eydelman, I; Zehavi, N; Feinshtein, V; Kumar, D
Cannabidiol (CBD) has previously been shown to inhibit inflammatory cytokine production in both in vitro and in vivo studies of neurodegenerative diseases. To date, the CBD treatment of these diseases by quantitative targeting directly to the brain is one of the greatest challenges. In this paper, we present a new particulate system capable of delivering CBD into the brain via the intranasal route. Intranasal administration of CBD-loaded starch nanoparticles resulted in higher levels of cannabidiol in the brain compared to an identically administered cannabidiol solution. The production and the characterization of starch-based nanoparticles was reported, as well as the evaluation of their penetration and anti-inflammatory activity in cells. Cannabidiol-loaded starch nanoparticles were prepared by crosslinking with divanillin, using the nanoprecipitation method. Evaluation of the anti-inflammatory activity in vitro was performed using the BV2 microglia cell line. The starch nanoparticles appeared under electron microscopy in clusters sized approximately 200 nm in diameter. In cultures of lipopolysaccharide-induced inflamed BV2 cells, the cannabidiol-loaded starch nanoparticles demonstrated low toxicity while effectively reducing nitric oxide production and IL-6 levels. The anti-inflammatory effect was comparable to that of a glucocorticoid. Starch-based nanoparticle formulations combined with intranasal administration may provide a suitable platform for efficacious cannabidiol delivery and activity in the central nervous system.
The Complementary Exponentiated Lomax-Poisson Distribution with Applications to Bladder Cancer and Failure Data
(2021-07) Kumar, D; Nassar, M
A new continuous four-parameter lifetime distribution is introduced by compounding the distribution of the maximum of a sequence of an independently identically expo nentiated Lomax distributed random variables and zero truncated Poisson random vari able, de ned as the complementary exponentiated Lomax Poisson (CELP) distribution. The new distribution which exhibits decreasing and upside down bathtub shaped density while the distribution has the ability to model lifetime data with decreasing, increasing and upside-down bathtub shaped failure rates. The new distribution has a number of well-known lifetime special sub-models, such as Lomax-zero truncated Poisson distribu tion, exponentiated Pareto-zero truncated Poisson distribution and Pareto- zero truncated Poisson distribution. A comprehensive account of the mathematical and statistical prop erties of the new distribution is presented. The model parameters are obtained by the methods of maximum likelihood, least squares, weighted least squares, percentiles, max imum product of spacing and Cramer-von-Mises and compared them using Monte Carlo simulation study. We illustrate the performance of the proposed distribution by means of two real data sets and both the data sets show the new distribution is more appropriate as compared to the transmuted Lomax, beta exponentiated Lomax, McDonald Lomax, Kumaraswamy Lomax, Weibull Lomax, Burr X Lomax and Lomax distributions.
Concurrent exposure to light and traffic noise pollution augments behavioral deficits in rats: Protective effect of curcumin
(2024-04) Sharma, S; Singh, J; Kumar, D; Arora, M; Jangra, A
The current study was aimed to explore the effect of light and traffic noise pollution exposure on behavioral and neurochemical deficits in adult male Wistar rats. We hypothesized that exposure to these pollutants alone or in combination may cause behavioral deficits in rats. Animals were allocated into different groups and were exposed to either varying light cycles (150 lx-5 lx, 14:10 h) or traffic noise with sound level 100 dBA for 6 h/day or both for 28 days. Concurrently, Curcumin (100 mg/kg; per oral; 28 days) was administered in varying light cycles and noise exposed animal groups to study the preventive effect. After the exposure time, the Morris water maze test, elevated plus maze test, novel object recognition test, locomotor activity test, and forced swimming test were conducted. The neurobehavioral results suggested that only combined exposure of light and noise pollution markedly impaired the learning as well as spatial and reference memory in rats. Furthermore, we found significant anxiety (less no. of open arm entries in EPM) and depressive-like behavior (increased immobility time) in light or/and traffic noise pollution exposed group animals in comparison to normal control groups. Curcumin treatment significantly prevented the behavioral anomalies in combined light and noise exposed rats. Furthermore, our results revealed augmentation of acetylcholinesterase, oxidative stress markers and interleukin (IL)-1β in the hippocampal region of noise and light-exposed groups which were markedly ameliorated by curcumin. In conclusion, our results suggest that curcumin significantly modulates light and noise pollution exposed behavioral and neurochemical anomalies in rats.
DeepAProt: Deep learning based abiotic stress protein sequence classification and identification tool in cereals
(2023-01) Ahmed, B; Haque, M; Kumar, D; Rai, A
The impact of climate change has been alarming for the crop growth. The extreme weather conditions can stress the crops and reduce the yield of major crops belonging to Poaceae family too, that sustains 50% of the world’s food calorie and 20% of protein intake. Computational approaches, such as artificial intelligence-based techniques have become the forefront of prediction-based data interpretation and plant stress responses. In this study, we proposed a novel activation function, namely, Gaussian Error Linear Unit with Sigmoid (SIELU) which was implemented in the development of a Deep Learning (DL) model along with other hyper parameters for classification of unknown abiotic stress protein sequences from crops of Poaceae family. To develop this models, data pertaining to four different abiotic stress (namely, cold, drought, heat and salinity) responsive proteins of the crops belonging to poaceae family were retrieved from public domain. It was observed that efficiency of the DL models with our proposed novel SIELU activation function outperformed the models as compared to GeLU activation function, SVM and RF with 95.11%, 80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity, respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid. res.in:5500/) was developed using flask API, along with its mobile app. This server/App will provide researchers a convenient tool, which is rapid and economical in identification of proteins for abiotic stress management in crops Poaceae family, in endeavour of higher production for food security and combating hunger, ensuring UN SDG goal 2.0.
Determining the optimal duration of plyometric training for enhancing vertical jump performance: a systematic review and meta analysis
(2023) Kumar, D; Dhull, S; Nara, K; Kumar, P
Introduction. Plyometric training is done in many ways, but countermovement jumps and squat jumps are easy and safe methods involving minimal use of equipment to increase lower body muscular power. There is enough evidence in the literature to support the notion that plyometric training increases vertical jump height. But the combination of countermovement jumps and squat jumps and its impact on vertical jump height is still unclear. Purpose. Therefore, to examine the importance of various plyometric training techniques, this meta-analytical evaluation was created. Materials and Methods. PubMed, MEDLINE, and Google Scholar databases were searched through August 2022. Empirical studies are expressed in the English language and issued within the last twenty-eight years. Included squat jump and countermovement jump interventions for improving vertical jump height. The studies used a training duration of a minimum of four weeks or above involving a control group included. As an approach to plyometric training that raises vertical jump height, the effects of countermovement jump and squat jump training were evaluated. To assess the level of heterogeneity, the I2 statistics were utilized. The value of I2 above 75% indicates significant heterogeneity and leads to the subgroup analysis method. Summary effects were calculated using standardized mean difference. With the help of the Cochrane tool, the risk of bias was assessed. Results. The 22 study results all met the enclosure criteria. The meta-analysis indicated that the total impact of squat jumps training had high effects on vertical jump height [Effect Size = 0.96, (95% CI,0.57 to 1.35)]. A moderate degree [Effect Size = 0.91, (95 % CI,0.46 to 1.37)] of effects of countermovement jumps training on vertical jump height was reported by included interventions. Lesser than 10 weeks of plyometric training intervention duration they probably had a small effect on countermovement jumps [Effect Size = 0.79, (95 % CI, 0.30 to 1.29)]. Conclusion: Squat jumps and countermovement jumps are an efficient kind of training to increase the vertical jump height of male and female athletes, according to our study on plyometric training. Higher posttests mean differences were found in the studies including male subjects. The length of intervention has proportional effects on vertical jump height the recommended length of training should lie between 8 to 12 weeks. The effect size ranged between small to large degrees was observed in overall and subgroup analysis.
Determining the optimal duration of plyometric training for enhancing vertical jump performance: a systematic review and meta analysis
(2023) Kumar, D; Dhull, S
Introduction. Plyometric training is done in many ways, but countermovement jumps and squat jumps are easy and safe methods involving minimal use of equipment to increase lower body muscular power. There is enough evidence in the literature to support the notion that plyometric training increases vertical jump height. But the combination of countermovement jumps and squat jumps and its impact on vertical jump height is still unclear. Purpose. Therefore, to examine the importance of various plyometric training techniques, this meta-analytical evaluation was created. Materials and Methods. PubMed, MEDLINE, and Google Scholar databases were searched through August 2022. Empirical studies are expressed in the English language and issued within the last twenty-eight years. Included squat jump and countermovement jump interventions for improving vertical jump height. The studies used a training duration of a minimum of four weeks or above involving a control group included. As an approach to plyometric training that raises vertical jump height, the effects of countermovement jump and squat jump training were evaluated. To assess the level of heterogeneity, the I2 statistics were utilized. The value of I2 above 75% indicates significant heterogeneity and leads to the subgroup analysis method. Summary effects were calculated using standardized mean difference. With the help of the Cochrane tool, the risk of bias was assessed. Results. The 22 study results all met the enclosure criteria. The meta-analysis indicated that the total impact of squat jumps training had high effects on vertical jump height [Effect Size = 0.96, (95% CI,0.57 to 1.35)]. A moderate degree [Effect Size = 0.91, (95 % CI,0.46 to 1.37)] of effects of countermovement jumps training on vertical jump height was reported by included interventions. Lesser than 10 weeks of plyometric training intervention duration they probably had a small effect on countermovement jumps [Effect Size = 0.79, (95 % CI, 0.30 to 1.29)].
Different Classical Methods of Estimation and Chi-squared Goodness-of- t Test for Unit Generalized Inverse Weibull Distribution
(2021-07) khaoula, A; Dey, S; Kumar, D
In this paper, we try to contribute to the distribution theory literature by incorporat ing a new bounded distribution, called the unit generalized inverse Weibull distribution (UGIWD) in the (0, 1) intervals by transformation method. The proposed distribution exhibits increasing and bathtub shaped hazard rate function. We derive some basic statis tical properties of the new distribution. Based on complete sample, the model parameters are obtained by the methods of maximum likelihood, least square, weighted least square, percentile, maximum product of spacing and Cramer-von-Mises and compared them using Monte Carlo simulation study. In addition, bootstrap condence intervals of the param eters of the model based on aforementioned methods of estimation are also obtained. We illustrate the performance of the proposed distribution by means of one real data set and the data set shows that the new distribution is more appropriate as compared to unit Birnbaum-Saunders, unit gamma, unit Weibull, Kumaraswamy and unit Burr III distri butions. Further, we construct chi-squared goodness-of- t tests for the UGIWD using right censored data based on Nikulin-Rao-Robson (NRR) statistic and its modi cation. The criterion test used is the modi ed chi-squared statistic Y 2, developed by Bagdon avicius and Nikulin (2011) for some parametric models when data are censored. The performances of the proposed test are shown by an intensive simulation study and an application to real data set
Genome-Wide Analysis and Evolutionary Perspective of the Cytokinin Dehydrogenase Gene Family in Wheat
(2022-08) Jain, P; Singh, A; Iquebal, M; Jaiswal, S; Kumar, S; Kumar, D
Cytokinin dehydrogenase (CKX; EC.1.5.99.12) regulates the level of cytokinin (CK) in plants and is involved in CK regulatory activities. In different plants, a small gene family encodes CKX proteins with varied numbers of members. The segenes are expanded in the genome mainly due to segmental duplication events. Despite their biological importance, CKX genes in Triticum aestivum have yet to be studied in depth. A total of 11 CKX subfamilies were identified with similar gene structures, motifs, domains, cis-acting elements, and an average signal peptide of 25 amino acid length was found. Introns, ranging from one to four, were present in the coding regions at a similar interval in major CKX genes. Putative cis-elements such as abscisic acid, auxin, salicylic acid, and low temperature-, drought-, and light-responsive cis-regulatory elements were found in the promoter region of majority CKX genes. Variation in the expression pattern of CKX genes were identified across different tissues in Triticum. Phylogenetic analysis shows that the same subfamily of CKX clustered into a similar clade that reflects their evolutionary relationship. We performed a genome-wide identification of CKX family members in the Triticum aestivum genome to get their chromosomal location, gene structure, cis-element, phylogeny, synteny, and tissue- and stage-specific expression along with gene ontology. This study has also elaborately described the tissue- and stage-specific expression and is the resource for further analysis of CKX in the regulation of biotic and abiotic stress resistance, growth, and development in Triticum and other cereals to endeavor for higher production and proper management.
Hypoxia induced lactate acidosis modulates tumor microenvironment and lipid reprogramming to sustain the cancer cell survival
(2023-01) Singh, L; Nair, L; Kumar, D
It is well known that solid hypoxic tumour cells oxidise glucose through glycolysis, and the end product of this pathway is fermented into lactate which accumulates in the tumour microenvironment (TME). Initially, it was proclaimed that cancer cells cannot use lactate; therefore, they dump it into the TME and subsequently augment the acidity of the tumour milieu. Furthermore, the TME acts as a lactate sink with stope variable amount of lactate in different pathophysiological condition. Regardless of the amount of lactate pumped out within TME, it disappears immediately which still remains an unresolved puzzle. Recent findings have paved pathway in exploring the main role of lactate acidosis in TME. Cancer cells utilise lactate in the de novo fatty acid synthesis pathway to initiate angiogenesis and invasiveness, and lactate also plays a crucial role in the suppression of immunity. Furthermore, lactate re-programme the lipid biosynthetic pathway to develop a metabolic symbiosis in normoxic, moderately hypoxic and severely hypoxic cancer cells. For instance: severely hypoxic cancer cells enable to synthesizing poly unsaturated fatty acids (PUFA) in oxygen scarcity secretes excess of lactate in TME. Lactate from TME is taken up by the normoxic cancer cells whereas it is converted back to PUFAs after a sequence of reactions and then liberated in the TME to be utilized in the severely hypoxic cancer cells. Although much is known about the role of lactate in these biological processes, the exact molecular pathways that are involved remain unclear. This review attempts to understand the molecular pathways exploited by lactate to initiate angiogenesis, invasiveness, suppression of immunity and cause re-programming of lipid synthesis. This review will help the researchers to develop proper understanding of lactate associated bimodal regulations of TME.
Inference for the Two Parameter Reduced Kies Distribution under Progressive Type-II Censoring
(2020-11) Shrahili, M; Alotaibi, N; Kumar, D
In this paper, we obtained several recurrence relations for the single and product moments under progressively Type-II right censored order statistics and then use these results to compute the means and variances of two parameter reduced Kies distribution. Besides, these moments are then utilized to derived best linear unbiased estimators of the scale and location parameters of two parameter reduced Kies distribution. The parameters of the two parameter reduced Kies distribution are estimated under progressive type-II censoring scheme. The model parameters are estimated using the maximum likelihood estimation method. Further, we explore the asymptotic confidence intervals for the model parameters. Monte Carlo simulations are performed to compare between the proposed estimation methods under progressive type-II censoring scheme. Based on our study, we can conclude that maximum likelihood estimators is decreasing with respect to an increase of the schemes and comparing the three censoring schemes, it is clear that the mean sum of squares, confidence interval lengths are smaller for scheme 1 than schemes 2 and 3.
Inferences for generalized Topp-Leone distribution under dual generalized order statistics with applications to Engineering and COVID-19 data
(2021) Kumar, D; Nassar, M; Dey, S
This article accentuates the estimation of a two-parameter generalized Topp-Leone distribution using dual generalized order statistics (dgos). In the part of estimation, we obtain maximum likelihood (ML) estimates and approximate confidence intervals of the model parameters using dgos, in particular, based on order statistics and lower record values. The Bayes estimate is derived with respect to a squared error loss function using gamma priors. The highest posterior density credible interval is computed based on the MH algorithm. Furthermore, the explicit expressions for single and product moments of dgos from this distribution are also derived. Based on order statistics and lower records, a simulation study is carried out to check the efficiency of these estimators. Two real life data sets, one is for order statistics and another is for lower record values have been analyzed to demonstrate how the proposed methods may work in practice.
IoT-enabled technologies for controlling COVID-19 Spread: A scientometric analysis using CiteSpace
(2023-07) Kumar, D; Sood, S; Rawat, K
The COVID-19 outbreak has unleashed a cataclysmic impact on the daily existence of humanity, triggering a widespread upheaval in public health and wreaking havoc on the economies of affected nations. This lethal disease has emerged as a leading cause of substantial mortality and morbidity around the globe, and the emergence of new virus variants has confronted humanitarian society with formidable difficulties. In this tumultuous period, the Internet of Things (IoT) has played an indispensable role. IoT has offered various innovative solutions to curb the spread of the pandemic, providing a beacon of hope amidst the chaos. The current research delves into the literature on IoT-assisted COVID-19 research, utilizing a scientometric analysis to extensively examine the technological impact in the battle against the outbreak. It illuminates the multifaceted role of ICT in the ongoing pandemic by employing an array of empirical approaches such as publication patterns, citation structures, leading nations, literature co-citation network analysis, and knowledge mapping of scientific literature using the CiteSpace tool. Furthermore, the study uncovers the research frontiers, research hotspots, cluster analysis, and potential future directions in this knowledge domain, providing a visual narrative that inspires hope and a renewed commitment to our collective responsibility in the face of this global crisis.
New Genomic Regions Identified for Resistance to Spot Blotch and Terminal Heat Stress in an Interspecific Population of Triticum aestivum and T. spelta
(2022-11) Navathe, S; Pandey, A; Sharma, S; Chand, R; Mishra, V; Kumar, D
Wheat is one of the most widely grown and consumed food crops in the world. Spot blotch and terminal heat stress are the two significant constraints mainly in the Indo–Gangetic plains of South Asia. The study was undertaken using 185 recombinant lines (RILs) derived from the interspecific hybridization of ‘Triticum aestivum (HUW234) T. spelta (H+26)’ to reveal genomic regions associated with tolerance to combined stress to spot blotch and terminal heat. Different physiological (NDVI, canopy temperature, leaf chlorophyll) and grain traits (TGW, grain size) were observed under stressed (spot blotch, terminal heat) and non-stressed environments. The mean maturity duration of RILs under combined stress was reduced by 12 days, whereas the normalized difference vegetation index (NDVI) was 46.03%. Similarly, the grain size was depleted under combined stress by 32.23% and thousand kernel weight (TKW) by 27.56% due to spot blotch and terminal heat stress, respectively. The genetic analysis using 6734 SNP markers identified 37 significant loci for the area under the disease progress curve (AUDPC) and NDVI. The genome-wide functional annotation of the SNP markers revealed gene functions such as plant chitinases, NB-ARC and NBS-LRR, and the peroxidase superfamily Cytochrome P450 have a positive role in the resistance through a hypersensitive response. Zinc finger domains, cysteine protease coding gene, F-box protein, ubiquitin, and associated proteins, play a substantial role in the combined stress of spot blotch and terminal heat in bread wheat, according to genomic domains ascribed to them. The study also highlights T. speltoides as a source of resistance to spot blotch and terminal heat tolerance.
A new parameter estimation method for the extended power Lindley distribution based on order statistics, with application
(2024-06) Kumar, D; Kumar, M; Yadav, S; Goyal, A
In this paper, we propose inference procedures for the estimation of parameters by using or der statistics. First, we derive some new expressions for single and product moments of the order statistics from the extended power Lindley distribution. We then use these moments to obtain the best linear unbiased estimates (BLUEs) of the location and scale parameters based on Type-II right-censored samples. A real data set is analysed to illustrate the flexibility and importance of the model.
Parameter independent scheme for singularly perturbed problems including a boundary turning point of multiplicity ≥ 1
(2023-06) Kumari, P; Kumar, D
A numerical scheme is developed for parabolic singularly per turbed boundary value problems, including multiple boundary turning points at the left endpoint of the spatial direction. The highest order derivative of these problems is multiplied by a small parameter ε(0 < ε ≪ 1), and when it is close to zero, the solution exhibits a parabolic type boundary layer near the left lateral surface of the domain of consideration. Thus, large oscillations appear when classical/standard numerical methods are used to solve the prob lem, and one cannot achieve the expected accuracy. Thus, the Crank-Nicolson scheme on a uniform mesh in the temporal direction and an upwind scheme on a Shishkin-type mesh in the spatial direction is constructed. The theoretical analysis shows that the method converges irrespective of the size of ε with accuracy O((∆t)2 + N−1lnN). Three test examples are presented to verify that the computational results agree with the theoretical ones.
PARAMETER INDEPENDENT SCHEME FOR SINGULARLY PERTURBED PROBLEMS INCLUDING A BOUNDARY TURNING POINT OF MULTIPLICITY ≥ 1
(2023-06) Kumari, P; Kumar, D; Ramos, H
A numerical scheme is developed for parabolic singularly per turbed boundary value problems, including multiple boundary turning points at the left endpoint of the spatial direction. The highest order derivative of these problems is multiplied by a small parameter ε (0 < ε ≪ 1), and when it is close to zero, the solution exhibits a parabolic type boundary layer near the left lateral surface of the domain of consideration. Thus, large oscillations appear when classical/standard numerical methods are used to solve the prob lem, and one cannot achieve the expected accuracy. Thus, the Crank-Nicolson scheme on a uniform mesh in the temporal direction and an upwind scheme on a Shishkin-type mesh in the spatial direction is constructed. The theoretical analysis shows that the method converges irrespective of the size of ε with accuracy O((∆t) 2 + N −1 ln N). Three test examples are presented to verify that the computational results agree with the theoretical ones.
Power Modi ed Lindley Distribution: Properties, Classical and Bayesian Estimation and Regression Model with Applications
(2023-07) Kharazmi, O; Kumar, D
In this article, we explore a new probability density function, called the power modi ed Lindley distribution. Its main feature is to operate a simple trade-o among the general ized exponential, Weibull and gamma distributions, o ering an alternative to these three well-established distributions. The proposed model turns out to be quite exible: its probability density function can be right skewed and its associated hazard rate function may be increasing, decreasing, unimodal and constant. First the model parameters of the proposed distribution are obtained by the maximum likelihood method. Next, Bayes estimators of the unknown parameters are obtained under di erent loss functions. In addi tion, bootstrap condence intervals are provided to compare with Bayes credible intervals. Besides, log power modi ed Lindley regression model for censored data is proposed. Two real data sets are analyzed to illustrate the exibility and importance of the proposed model.
Power Modified Lindley Distribution: Properties, Classical and Bayesian Estimation and Regression Model with Applications
(2023-07) Kumar, D; Dey, S; Kharazmi, O
In this article, we explore a new probability density function, called the power modified Lindley distribution. Its main feature is to operate a simple trade-off among the general ized exponential, Weibull and gamma distributions, offering an alternative to these three well-established distributions. The proposed model turns out to be quite flexible: its probability density function can be right skewed and its associated hazard rate function may be increasing, decreasing, unimodal and constant. First the model parameters of the proposed distribution are obtained by the maximum likelihood method. Next, Bayes estimators of the unknown parameters are obtained under different loss functions. In addi tion, bootstrap confidence intervals are provided to compare with Bayes credible intervals. Besides, log power modified Lindley regression model for censored data is proposed. Two real data sets are analyzed to illustrate the flexibility and importance of the proposed model.

Browsing by Author "Kumar, D"

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