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During the raw silk degumming process, the water-soluble natural protein sericin is discarded as waste material in the sericulture industry of Bangladesh. In this study, various amount of sericin (as nutritional supplement) treated mulberry leaves was fed to three different silkworm groups and its effect on cocoon, pupal parameters as well as grainase properties of the silkworms was evaluated. The results indicated that fecundity increased with the decrease of the concentration of sericin for all treatment groups. There was no significant difference among the treatment groups and the control group for fecundity value, but in the case of hatching percentage (%), all the treatment groups showed the reverse trend with fecundity. Cocoon production was increased by 23.91% when silkworm was fed with 0.50% sericin-treated mulberry leaves at chawki stage (first to the third instar) compared to the control group.

The COVID-19 (SARS-CoV-2 virus) pandemic has led to a substantial loss of human life worldwide by providing an unparalleled challenge to the public health system. The economic, psychological, and social disarray generated by the COVID-19 pandemic is devastating. Public health experts and epidemiologists worldwide are struggling to formulate policies on how to control this pandemic as there is no effective vaccine or treatment available which provide long-term immunity against different variants of COVID-19 and to eradicate this virus completely. As the new cases and fatalities are recorded daily or weekly, the responses are likely to be repeated or longitudinally correlated. Thus, studying the impact of available covariates and new cases on deaths from COVID-19 repeatedly would provide significant insights into this pandemic's dynamics. For a better understanding of the dynamics of spread, in this paper, we study the impact of various risk factors on the new cases and deaths over time. To do that, we propose a marginal-conditional based joint modelling approach to predict trajectories, which is crucial to the health policy planners for taking necessary measures. The conditional model is a natural choice to study the underlying property of dependence in consecutive new cases and deaths. Using this model, one can examine the relationship between outcomes and predictors, and it is possible to calculate risks of the sequence of events repeatedly. The advantage of repeated measures is that one can see how individual responses change over time. The predictive accuracy of the proposed model is also compared with various machine learning techniques. The machine learning algorithms used in this paper are extended to accommodate repeated responses. The performance of the proposed model is illustrated using COVID-19 data collected from the Texas Health and Human Services.

Bivariate time-series counts with excessive zeros frequently occur in medical, biological, social, and environmental sciences. An appropriate analysis of such data must account for serial correlation over time, cross correlation between bivariate responses, and excessive zeros. We first introduce a serially correlated random effect series shared by both responses to account for a common temporal trend. The shared temporal random effects can also characterize the correlation between two responses. Given the common temporal random effects, we incorporate a compound Poisson random effect series into a Poisson model for each response to capture excessive zeros and additional variation. Our approach models zero and positive parts of the responses in an integral way. Our approach also unifies marginal modeling and conditional modeling analyses. We develop a quasi-likelihood approach for the estimation of the model parameters. Our method is illustrated with the analysis of musculoskeletal and non-musculoskeletal workplace injuries recorded every four weeks on a group of hospital cleaners over a seven-year period. Performance of the proposed model is evaluated through simulation studies.

Fine particulate matter ( $$\text{ PM}_{2.5}$$ ), tiny particles in the air, is air contamination that negatively impacts the environment and human health when levels in the air are high. The elevated level of $$\text{ PM}_{2.5}$$ also reduces visibility and causes the air to appear hazy. Due to its impact on environment and health, almost every country around the world keeps track of $$\text{ PM}_{2.5}$$ air quality level and records the data repeatedly over time in many sites. As the data are collected repeatedly, there is likely to be a natural dependency among the repeated measures of $$\text{ PM}_{2.5}$$ level in a specific site. Modeling and analyzing these repeated data will help policymakers recommend new policies and/or update existing policies. Thus adequate modeling of such data is of enormous interest among the researchers and policymakers. It is noteworthy that as the data are collected repeatedly in immense volume, big data modeling techniques are required for modeling such data. This paper proposed a new modeling framework to analyze and trajectory risk prediction of categorical responses from big data collected repeatedly. We developed a divide and recombine approach to analyzing big data gathered continually. We used the Markov model for data division, and the Markov chain is used to recombine the marginal and conditional probabilities and estimated joint probabilities for trajectory. We illustrated the proposed model using $$\text{ PM}_{2.5}$$ outdoor air pollution data from the United States between the years 2000 to 2020. The performance of the proposed methodology is also checked through bootstrap simulation studies. The proposed methodology will be useful to analyze and trajectory risk prediction of repeatedly measured responses from big data from various fields.

Three rod-shaped alkyloxy substituted 4-chloroazobenzene liquid crystals, 1-(4-chlorophenyl)-2-[4- (alkyloxy)phenyl]diazene (hexayl, octyl and nonyl as flexible oxyalkyl chain) have been synthesized by diazotization of p-chloroaniline with phenol and subsequently performed etherification reaction with different alkyl bromides. The structures of the substituted 4-chloroazobenzene liquid crystals have been characterized by spectroscopic methods. The mesomorphic properties of the liquid crystals were examined by polarizing optical microscope (POM) and differential scanning calorimetry (DSC). All the oxyalkyl homologues of chloro substituted azobenzene showed enantiotropic smectic A (SmA) mesophase, which was understood clearly by the texture of the compounds employing polarizing optical microscope (POM) analysis. During heating scan in DSC analyses melting points, SmA-isotropic temperature and enthalphy changes associated with SmA-isotropic transition showed a remarkable impact on the spacer length of 4-chloro azobenzene derivatives.

Azidoethyl-2-bromo-2-methylpropanoate (AEBMP) has been synthesized from 2-azidoethanol and 2-bromo-2-methylpropionyl bromide under nitrogen atmosphere. The azido end-functional polystyrene (PS-N3) has been synthesized by Atom Transfer Radical Polymerization (ATRP) technique using AEBMP initiator and styrene in conjugation with Cu(1)Br-bipyridine catalyst at 100°C. The polymerization was performed at three different periods of time, and it was found that both yield and number average molecular weight were increased linearly with increasing reaction time. Thermogravimetric analysis showed that the polystyrene is stable up to 300°C. The molecular weight of the polystyrene was determined using Gel Permeation Chromatography (GPC), and structures of the initiator AEBMP and polystyrene (PS-N3) were characterized by 1H NMR and FT-IR spectroscopy.

The whole world is devastated by the impact of the COVID-19 pandemic. The socioeconomic and other effects of COVID-19 on people are visible in all echelons of society. The main goal of countries is to stop the spreading of this pandemic by reducing the COVID-19 related new cases and deaths. In this paper, we analyzed the correlated count outcomes, daily new cases, and fatalities, and assessed the impact of some covariates by adopting a generalized bivariate Poisson model. There are different effects of duration on new cases and deaths in different countries. Also, the regional variation found to be different, and population density has a significant impact on outcomes.

The current statistical modeling of coronavirus (COVID-19) spread has mainly focused on spreading patterns and forecasting of COVID-19 development; these patterns have been found to vary among locations. As the survival time of coronaviruses on surfaces depends on temperature, some researchers have explored the association of daily confirmed cases with environmental factors. Furthermore, some researchers have studied the link between daily fatality rates with regional factors such as health resources, but found no significant factors. As the spreading patterns of COVID-19 development vary a lot among locations, fitting regression models of daily confirmed cases or fatality rates directly with regional factors might not reveal important relationships. In this study, we investigate the link between regional spreading patterns of COVID-19 development in Italy and regional factors in two steps. First, we characterize regional spreading patterns of COVID-19 daily confirmed cases by a special patterned Poisson regression model for longitudinal count; the varying growth and declining patterns as well as turning points among regions in Italy have been well captured by regional regression parameters. We then associate these regional regression parameters with regional factors. The effects of regional factors on spreading patterns of COVID-19 daily confirmed cases have been effectively evaluated.

Briquetting is a continuous compaction process to transform loose powders into solid briquettes. The density and strength of formed briquettes vary significantly in the process, affecting the quality of products. This work presented a numerical modelling of briquetting based on Finite Element Method (FEM), focusing on the evolutions of briquette structure and stress. The mechanical behaviour of powders was characterised by the density independent Drucker-Prager Cap (DPC) model. The parameters in the DPC model were determined by conducting experimental die compaction on iron ore fines. The stress distributions inside the briquettes at the maximum compression and ejection stages were analysed. The relative density, Von Mises stress and hydrostatic pressure showed clear declines from the top to the bottom. The effects of powder-wall friction and feeding pressure on briquetting were studied. The average relative density and power draw increased almost linearly with increasing feeding pressure. However, a nonlinear trend was observed with increasing friction. The results suggested that an optimal briquetting process could be achieved by selecting proper boundary conditions.

Iminoyl N-heterocyclic carbine ligand with cationic allyl Pd(II) complex (3) was successfully synthesized by transmetallation of corresponding Ag complex with one equivalent of [Pd(allyl)(COD)]+SbF6–. A slightly distorted square planer structure of the Pd(II) complex 3 was confirmed by single crystal X-ray diffraction analysis. The Pd(II) complex 3 is stable in air and found to show moderate activity in the polymerization of norbornene without any activator. The polynorbornene produced with Pd(II) complex 3 was obtained to be stable up to 440 °C. The 1H and 13C NMR spectra of the polymer indicated addition polymerization of norbornene and the presence of the vinylene group at the end of polymer chain.

The media upholds democracy and rights of mass people in the contemporary globe, and it is regarded as the fourth estate of a state. It is known to all that freedom of press is a fundamental right of citizens in a democracy. This fundamental right is being questioned by world governments bearing manifold interpretation in the related laws and systems along with different kinds of insecurity issues like killing, force disappearance, attacking or being threatened. Mass-media systems of the world vary from one another according to the economy, polity, religion, and culture of different societies. Journalism and freedom of speech has been an important part of a democratic society from the very beginning of modern history. Violence, threats, and insecurity against journalists all over the world have been increasing steadily. Additionally, fear of losing jobs, political pressure and harassment by the state-authority or political leaders, providing anti-public law and self-censorship are suffocating media freedoms every now and then. A good number of researches have been done; and published literature on the subject matter is also ample. But there are very few on the issue of media-control and journalist insecurity in Bangladesh. This research is an attempt to explore the journalists’ insecurity issues and overt/covered control mechanisms by the state that has been practiced over the mainstream news media in Bangladesh in different times. The study mostly focused on the socio-political environment of journalists’ insecurity and state-sponsored media-control mechanisms in Bangladesh. There were possibilities to work with other countries, but the area has been chosen based on Bangladeshi Media to add something new to that gap. Os meios de comunicação social defendem a democracia e os direitos das pessoas de massas no mundo contemporâneo, e é considerado como o quarto estado. Todos sabem que a liberdade de imprensa é um direito fundamental dos cidadãos numa democracia. Este direito fundamental está a ser questionado por governos mundiais com múltiplas interpretações nas leis e sistemas relacionados, juntamente com diferentes tipos de questões de insegurança como matar, forçar o desaparecimento, atacar ou ser ameaçado. Os sistemas de mass-media do mundo variam uns dos outros de acordo com a economia, política, religião e cultura de diferentes sociedades. O jornalismo e a liberdade de expressão têm sido uma parte importante de uma sociedade democrática desde o início da história moderna. A violência, as ameaças e a insegurança contra os jornalistas em todo o mundo têm vindo a aumentar constantemente. Além disso, o medo de perder empregos, a pressão política e o assédio por parte da autoridade do Estado ou dos líderes políticos, proporcionando leis anti-públicas e auto-censura, sufocam de vez em quando as liberdades dos meios de comunicação social. Um bom número de pesquisas tem sido feito; e a literatura publicada sobre o assunto também é ampla. Mas há muito poucos sobre a questão do controlo dos media e da insegurança dos jornalistas no Bangladesh. Esta investigação é uma tentativa de explorar as questões de insegurança dos jornalistas e os mecanismos de controlo aberto/coberto pelo Estado que tem sido praticado sobre os principais meios de comunicação social no Bangladesh em diferentes épocas. O estudo centrou-se principalmente no ambiente sócio-político da insegurança dos jornalistas e nos mecanismos de controlo dos meios de comunicação social patrocinados pelo Estado no Bangladesh. Havia possibilidades de trabalhar com outros países, mas a área foi escolhida com base nos meios de comunicação social do Bangladesh para acrescentar algo de novo a essa lacuna.

Generalized linear mixed models have played an important role in the analysis of longitudinal data; however, traditional approaches have limited flexibility in accommodating skewness and complex correlation structures. In addition, the existing estimation approaches generally rely heavily on the specifications of random effects distributions; therefore, the corresponding inferences are sometimes sensitive to the choice of random effect distributions under certain circumstance. In this paper, we incorporate serially dependent distribution-free random effects into Tweedie generalized linear models to accommodate a wide range of skewness and covariance structures for discrete and continuous longitudinal data. An optimal estimation of our model has been developed using the orthodox best linear unbiased predictors of random effects. Our approach unifies population-averaged and subject-specific inferences. Our method is illustrated through the analyses of patient-controlled analgesia data and Framingham cholesterol data.

Summary Osteoporotic fractures are known to be highly recurring. We investigate bone-dependent and bone-independent risk factors of osteoporotic fracture frequency and relative proportions at various body locations by using the data from the osteoporotic fracture study that was conducted by the National Health and Nutrition Examination Survey, 2007–2008. We propose a new zero-inflated baseline category multinomial mixed model to characterize the clustered count responses and multinomial proportions by subject simultaneously while taking account of zero inflation and randomness of cluster sizes. Our approach gives additional insights into the risk factors of osteoporotic fracture frequencies at various body locations. This joint modelling of fracture frequency also allows us to characterize relative proportion patterns at various body locations by subject between men and women across age. These findings have clear policy relevance to appropriate osteoporotic fracture prevention and resource allocation.

Roadkill is of ecological importance so that there is increasing academic research to understand the causes and patterns of roadkills and their impact on ecosystems. This work is motivated by the study on roadkills of endangered Bufo calamita (B. calamita) (The natterjack toad) out of amphibian roadkills. The status of B. calamita is regarded as unfavorable due to large population declines. In the mentioned study, B. calamita and total amphibian roadkills were recorded via distance sampling on a National Road of Southern Portugal between March 1995 and March 1997. The traditional binomial modeling of these data are challenged by three issues. First, the zeros in B. calamita counts far exceeded its nominal level. Second, there is likely serial correlation among observations along the road. Finally, there is varying number of total amphibian roadkills at each sampling location; therefore, there is likely randomness in the number of total amphibian roadkills. All these features may contribute to overdispersion in the binomial observations. These three issues are routinely addressed one at a time separately, the first through zero-inflated binomial models, the second, for example, by means of random effects models for serially correlated binomial data and the third by models for binomial data with random cluster sizes. Therefore the data cannot be adequately modeled by any of these separate models. In this paper, we propose a new model to tackle these three issues simultaneously in the binomial analysis of B. calamita roadkills out of amphibian roadkills. Our approach is generally applicable to other binomial data with these three features.

Paired data have been widely collected in the efficiency studies of a new method against an established method in environmental, ecological and medical studies. For example, in comparative fishing studies, ability of catching target species (fish catch) or reducing the catch of non-target species (fish bycatch) is usually investigated through a paired design. These paired fish catches by weight are generally skewed and continuous, but with a significant portion of exact zeros (no catch). Such zero-inflated continuous data are traditionally handled by two-part models where the zero and positive components are handled separately; however, this separation generally destroys paired structure, and thus may result in substantial difficulty in characterizing the relative efficiency between two methods. To overcome this problem, we consider compound Poisson mixed model for paired data with which the zero and non-zero components are characterized in an integral way. In our approach, the clustering effects ...

Text summarization is a massive research area in natural language processing. It reduces the larger text and provided the prime meaning of a text document. Find the meaning of the larger text needed of a proper text analysis which gives a better text summarizer. Abstractive text summarizer gives a summary which can present or not present in the text document. The machine produces a text summary after learning from the human given summary. Sentence similarity is a way to judge a better text summarizer. It is exploring the similarity between sentences or words. This paper we discuss several methods of sentence similarity and proposed a method for identifying a better Bengali abstractive text summarizer. We used human given summary and machine response summary sentences for similarity measurement where both sentences contain a Bengali short text. There are several approaches to English sentences similarity measurement, and we applied some of the approaches for similarity measure for our Bengali text which give a satisfying result. For our given methods we collect data from online and social media and create a summary of those texts. After creating a summary pre-processing this text and generate a summary from our abstractive text summarization model. All summary sentence similarity measurement cases using the method provided an effective value and optimal result.

An E-shaped micro strip patch antenna is designed and analyzed in this paper which operates in MICS (402.0-406.0MHz) band. The Performance has been observed on a body of human phantom model as well as in free space with different conducting material for the designed antenna. The height of this antenna is taken 1.5mm from the ground plane. At resonance frequency of 405 MHz the S11 parameter is obtained in free space is -23.26dB for conducting material of aluminum and -17.96dB is measured on human phantom body at 405 MHz of resonance frequency. FR4 is used as substrate material. The Specific Absorption Rate (SAR) is found to be 0.3562 W/kg by placing the antanna on human phantom model. VSWR and directivity has been analyzed also.

Serially correlation binomial data with random cluster sizes occur frequently in environmental and health studies. Such data series have traditionally been analyzed using binomial state-space or hidden Markov models without appropriately accounting for the randomness in the cluster sizes. To characterize correlation and extra-variation arising from the random cluster sizes properly, we introduce a joint Poisson state-space modelling approach to analysis of binomial series with random cluster sizes. This approach enables us to model the marginal counts and binomial proportions simultaneously. An optimal estimation of our model has been developed using the orthodox best linear unbiased predictors. This estimation method is computationally efficient and robust since it depends only on the first- and second- moment assumptions of unobserved random effects. Our proposed approach is illustrated with analysis of birth delivery data.

Clustered multinomial data with random cluster sizes commonly appear in health, environmental and ecological studies. Traditional approaches for analyzing clustered multinomial data contemplate two assumptions. One of these assumptions is that cluster sizes are fixed, whereas the other demands cluster sizes to be positive. Randomness of the cluster sizes may be the determinant of the within-cluster correlation and between-cluster variation. We propose a baseline-category mixed model for clustered multinomial data with random cluster sizes based on Poisson mixed models. Our orthodox best linear unbiased predictor approach to this model depends only on the moment structure of unobserved distribution-free random effects. Our approach also consolidates the marginal and conditional modeling interpretations. Unlike the traditional methods, our approach can accommodate both random and zero cluster sizes. Two real-life multinomial data examples, crime data and food contamination data, are used to manifest our pro...

Atom Transfer Radical Polymerization (ATRP) has proven to be a powerful technique to obtain polymers with narrow polydispersities and controlled molecular weight. It also offers control over chain-ends. N-allyl-2-bromopropinamide (ABPN) was synthesized by one step reaction which involved on amidification of 2-bromo-propionyl bromide with allyl amine. 1H NMR has confirmed the structure of the ABPN. The well-defined polystyrene was synthesized by using ABPN as initiator in ATRP method. The results of the polymerization were significantly affected by reaction conditions. The vinyl end-functional polystyrene obtained was characterized by Gel Permeation Chromatography (GPC) and NMR analysis.

Longitudinal count data with excessive zeros frequently occur in social, biological, medical, and health research. To model such data, zero-inflated Poisson (ZIP) models are commonly used, after separating zero and positive responses. As longitudinal count responses are likely to be serially correlated, such separation may destroy the underlying serial correlation structure. To overcome this problem recently observation- and parameter-driven modelling approaches have been proposed. In the observation-driven model, the response at a specific time point is modelled through the responses at previous time points after incorporating serial correlation. One limitation of the observation-driven model is that it fails to accommodate the presence of any possible over-dispersion, which frequently occurs in the count responses. This limitation is overcome in a parameter-driven model, where the serial correlation is captured through the latent process using random effects. We compare the results obtained by the two m...

In this paper, A standardized and implantable planar inverted f antenna is accomplished with modified design, featuring balanced size miniaturization for wireless communications. This antenna has an intensified reliability and feasibility in turns for performing for biomedical application. The proposed antenna is represented For MICS BAND. MICS band contains 402 to 405 MHz resonant frequency. This band has been chosen because of its tractability towards higher bandwidth with the decreasing size of the antenna. 12.5 × 12.5mm2 miniature dimension is offered this antenna. The antenna is observed a better resonant performance at different substrate material. Four different types of substrate materials as Rogers RO3010, Rogers RT3210, Rogers RT6010 and Alumina were used in order to design the antenna to observe which one yields a better performance for the desired antenna. The purpose of using those comparisons is to find the lowest return loss and also get an acceptable efficiency which causes the higher gain in the desired. antenna. Return losses, Radiation Patterns, S-Parameters is observed and analyzed the antenna performances by using CST Microwave Studio.

Generalized linear mixed models have played an important role in the analysis of longitudinal data; however, traditional approaches have limited flexibility in accommodating skewness and complex correlation structures. In addition, the existing estimation approaches generally rely heavily on the specifications of random effects distributions; therefore, the corresponding inferences are sometimes sensitive to the choice of random effect distributions under certain circumstance. In this paper, we incorporate serially dependent distribution-free random effects into Tweedie generalized linear models to accommodate a wide range of skewness and covariance structures for discrete and continuous longitudinal data. An optimal estimation of our model has been developed using the orthodox best linear unbiased predictors of random effects. Our approach unifies population-averaged and subject-specific inferences. Our method is illustrated through the analyses of patient-controlled analgesia data and Framingham cholesterol data.

Precipitation is of great importance to agriculture, environment and ecosystem as a regular precipitation pattern is usually vital to healthy plants; excessive or insufficient rainfall can be harmful. Periodic patterns of precipitation can be studied based on regularly observed data over time. Since regularly observed precipitation data are generally skewed with many zeros, two common analysis approaches have been proposed recently. One approach investigates precipitation using a two-part model where the occurrence and positive amount of precipitation are analyzed separately (Piantadosi et al. in Environ Model Assess 14:431–438, 2009), whereas the other approach handles occurrence and amount simultaneously using a Tweedie’s compound Poisson model for independent observations (Hasan and Dunn in Int J Climatol 32:1006–1017, 2012). The former approach fails to maintain the regular temporal structure of serially observed precipitation, whereas the latter approach ignores serial dependence. As there is generally substantial serial correlation in the observed sequence of precipitation data over time, we introduce a compound Poisson state-space model with serially correlated random effects for daily precipitation data. This approach characterizes both occurrence and amount of precipitation simultaneously while accounting for the corresponding serial correlation. Our main results depend only on the first- and second-moment assumptions of unobserved random effects. We illustrate our method with the analysis of the daily precipitation data recorded at Mount Washington, NH, USA.

Graphite/aluminum metal matrix composite (MMC) joints are successfully fabricated by friction stir spot welding (FSSW). During lap-joining processes of aluminum alloy sheets by FSSW, graphite/water colloid is applied between the tool shoulder and the upper plate. The Raman spectrum of the FSSW joint confirms that graphite/aluminum MMC is successfully induced in the stir zone. Quasi-static shear tests and micro hardness measurements of the FSSW joint also show that the strength and toughness of the joint are significantly improved by inducing graphite/aluminum MMC in the stir zone. The results of the present study suggest that the mechanical properties of structural components joined by spot welds can be easily enhanced by adopting MMC joints by FSSW, instead of changing the material or increasing the number of joints.

Spot joints of ferritic 409L stainless steel are successfully fabricated by friction stir spot welding (FSSW) using a convex shoulder tool. The welding process, microstructure and failure of the FSSW joint are investigated experimentally. During the FSSW process, the Z-force history shows significant variations depending on the contact phenomena between the tool and the joined sheets, while the Z-torque history shows a rather steady increase without pronounced changes in the trend until the initiation of dwelling. Electron back-scatter diffraction suggests that both continuous dynamic recrystallization and recovery occurred in the stir zone during the FSSW process. Observation of the FSSW joint that failed under the given lap shear load shows that the cracks, which are the result of the interfaces between the upper and lower sheets, propagated into the weld along the interfacial surfaces, after which a necking/shear failure occurred. Finally, the rupture of the joint, which was initiated by the necking/shear failure, propagated along the circumference of the weld.

Time-series count data with excessive zeros frequently occur in environmental, medical and biological studies. These data have been traditionally handled by conditional and marginal modeling approaches separately in the literature. The conditional modeling approaches are computationally much simpler, whereas marginal modeling approaches can link the overall mean with covariates directly. In this paper, we propose new models that can have conditional and marginal modeling interpretations for zero-inflated time-series counts using compound Poisson distributed random effects. We also develop a computationally efficient estimation method for our models using a quasi-likelihood approach. The proposed method is illustrated with an application to air pollution-related emergency room visits. We also evaluate the performance of our method through simulation studies.

Analysis of longitudinal data with excessive zeros has gained increasing attention in recent years; however, current approaches to the analysis of longitudinal data with excessive zeros have primarily focused on balanced data. Dropouts are common in longitudinal studies; therefore, the analysis of the resulting unbalanced data is complicated by the missing mechanism. Our study is motivated by the analysis of longitudinal skin cancer count data presented by Greenberg, Baron, Stukel, Stevens, Mandel, Spencer, Elias, Lowe, Nierenberg, Bayrd, Vance, Freeman, Clendenning, Kwan, and the Skin Cancer Prevention Study Group[New England Journal of Medicine 323, 789-795]. The data consist of a large number of zero responses (83% of the observations) as well as a substantial amount of dropout (about 52% of the observations). To account for both excessive zeros and dropout patterns, we propose a pattern-mixture zero-inflated model with compound Poisson random effects for the unbalanced longitudinal skin cancer data. We also incorporate an autoregressive of order 1 correlation structure in the model to capture longitudinal correlation of the count responses. A quasi-likelihood approach has been developed in the estimation of our model. We illustrated the method with analysis of the longitudinal skin cancer data.

In medical and health studies, heterogeneities in clustered count data have been traditionally modeled by positive random effects in Poisson mixed models; however, excessive zeros often occur in clustered medical and health count data. In this paper, we consider a three-level random effects zero-inflated Poisson model for health-care utilization data where data are clustered by both subjects and families. To accommodate zero and positive components in the count response compatibly, we model the subject level random effects by a compound Poisson distribution. Our model displays a variance components decomposition which clearly reflects the hierarchical structure of clustered data. A quasi-likelihood approach has been developed in the estimation of our model. We illustrate the method with analysis of the health-care utilization data. The performance of our method is also evaluated through simulation studies.