local polynomial mixed effects models for longitudinal data we consider a nonparametric mixed effects model yi tij tij vi tij i tij j 1 2 ni i 1 2 n for longitudinal data we propose combining local polynomial kernel regression and linear mixed effects lme model techniques to estimate both fixedeffects population curve t and random effects curves vi t the resulting estimator called the local polynomial lme llme estimator takes the local correlation structure of the longitudinal data into account naturally we also propose new bandwidth selection strategies for estimating t and vi t simulation studies show that our estimator for t is superior to the existing estimators in the sense of mean squared errors the asymptotic bias variance mean squared errors and asymptotic normality are established for the llme estimators of t when ni is bounded and n tends to infinity our llme estimator converges in a standard nonparametric rate and the asymptotic bias and variance are essentially the same as those of the kernel generalized esti

small delay approximation of stochastic delay differential equations delay differential equations evolve in an infinite dimensional phase space in this paper we consider the effect of external fluctuations noise on delay differential equations involving one variable thus leading to univariate stochastic delay differential equations sdde s for small delays a univariate nondelayed stochastic differential equation approximating such a sdde is presented another approximation complementary to the first is also obtained using an average of the sdde s drift term over the delayed dynamical variable which defines a conditional average drift this second approximation is characterized by the fact that the diffusion term is identical to that of the original sdde for small delays our approach yields a steady state probability density and a conditional average drift which are in close agreement with numerical simulations of the original sdde we illustrate this scheme with the delayed linear langevin equation and a stochastic version of the delayed logistic equation the technique can be used with any type of noise and is easily generalized to multiple delays s1063 651x 99 08304 x

testing that a dependent process is uncorrelated an analysis is presented of a new testing procedure for the null hypothesis that a stochastic process is uncorrelated when the process is possibly dependent unlike with existing procedures the user does not need to choose any arbitrary number to implement the proposed test the asymptotic null distribution of the proposed test statistic is not standard but it is tabulated by means of simulations the test is compared with two alternative test procedures that require selection of user chosen numbers on the basis of asymptotic local power and finite sample behavior although the asymptotic local power of the proposed test is lower than those corresponding to the alternative tests in a monte carlo study i show that in small samples the test typically better controls the type i error and that the loss of power is not substantial

a pairwise interaction model for multivariate functional and longitudinal data functional data vectors consisting of samples of multivariate data where each component is a random function are encountered increasingly often but have not yet been comprehensively investigated we introduce a simple pairwise interaction model that leads to an interpretable and straightforward decomposition of multivariate functional data and of their variation into component specific processes and pairwise interaction processes the latter quantify the degree of pairwise interactions between the components of the functional data vectors while the component specific processes reflect the functional variation of a particular functional vector component that cannot be explained by the other components thus the proposed model provides an extension of the usual notion of a covariance or correlation matrix for multivariate vector data to functional data vectors and generates an interpretable functional interaction map the decomposition provided by the model can also serve as a basis for subsequent analysis such as study of the network structure of functional data vectors the decomposition of the total variance into componentwise and interaction contributions can be quantified by an formula see text like decomposition we provide consistency results for the proposed methods and illustrate the model by applying it to sparsely sampled longitudinal data from the baltimore longitudinal study of aging examining the relationships between body mass index and blood fats

effect of non gaussian noise sources in a noise induced transition here we study a noise induced transition when the system is driven by a noise source taken as colored and non gaussian we show using both a theoretical approximation and numerical simulations that there is a shift of the transition as the noise departs from the gaussian behavior also we confirm the reentrance effect found for colored gaussian noise and show the behavior of the transition line in the phase like diagram as the noise departs from gaussianity in the large correlation time limit

uniform convergence rates for nonparametric regression and principal component analysis in functional longitudinal data we consider nonparametric estimation of the mean and covariance functions for functional longitudinal data strong uniform convergence rates are developed for estimators that are local linear smoothers our results are obtained in a unified framework in which the number of observations within each curve cluster can be of any rate relative to the sample size we show that the convergence rates for the procedures depend on both the number of sample curves and the number of observations on each curve for sparse functional data these rates are equivalent to the optimal rates in nonparametric regression for dense functional data root n rates of convergence can be achieved with proper choices of bandwidths we further derive almost sure rates of convergence for principal component analysis using the estimated covariance function the results are illustrated with simulation studies

dual reciprocity bem bdim technique for conjugate heat transfer computations numerical simulation of conjugate heat transfer problems by methods based on the boundary element method bem promises great flexibility of the computational model a combination of dual reciprocity bem for computation of heat conduction in solid regions with boundary domain integral method for computation of heat transfer and fluid flow in fluid regions represents a promising way in numerical simulation of conjugate heat transfer it combines boundary only discretisation of solid parts and boundary and internal discretisation of fluid regions in order to efficiently apply iterative solvers the subdomain technique is used both in solid and in fluid parts of the computational domain the results are compared with those of a thermal shock problem achieved by a pure boundary domain integral formulation and is verified on the test case of conjugate natural convection in a cavity with conducting side wall

effects of time delay on symmetric two species competition subject to noise noise and time delay act simultaneously on real ecological systems the lotka volterra model of symmetric two species competition with noise and time delay was investigated in this paper by means of stochastic simulation we find that i the time delay induces the densities of the two species to periodically oscillate synchronously ii the stationary probability distribution function of the two species densities exhibits a transition from multiple to single stability as the delay time increases iii the characteristic correlation time for the sum of the two species densities squared exhibits a nonmonotonic behavior as a function of delay time our results have the implication that the combination of noise and time delay could provide an efficient tool for understanding real ecological systems

functional data analysis for sparse longitudinal data we propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data the method aims at irregularly spaced longitudinal data where the number of repeated measurements available per subject is small in contrast classical functional data analysis requires a large number of regularly spaced measurements per subject we assume that the repeated measurements are located randomly with a random number of repetitions for each subject and are determined by an underlying smooth random subject specific trajectory plus measurement errors basic elements of our approach are the parsimonious estimation of the covariance structure and mean function of the trajectories and the estimation of the variance of the measurement errors the eigenfunction basis is estimated from the data and functional principal components score estimates are obtained by a conditioning step this conditional estimation method is conceptually simple and straightforward to implement

numerical analysis of a new mixed formulation for eigenvalue convection diffusion problems a mixed formulation is proposed and analyzed mathematically for coupled convection diffusion in heterogeneous medias transfer in solid parts driven by pure diffusion is coupled with convection diffusion transfer in fluid parts this study is carried out for translation invariant geometries general infinite cylinders and unidirectional flows this formulation brings to the fore a new convection diffusion operator the properties of which are mathematically studied its symmetry is first shown using a suitable scalar product it is proved to be self adjoint with compact resolvent on a simple hilbert space its spectrum is characterized as being composed of a double set of eigenvalues one converging towards infty and the other towards infty thus resulting in a nonsectorial operator the decomposition of the convection diffusion problem into a generalized eigenvalue problem permits the reduction of the original three dimensional problem into a two dimensional one despite the operator being nonse

cross correlated bounded noises induced the population extinction and enhancement of stability in a population growth model abstract the bounded noises as a new typed non gaussian noise have important practical significance in this paper the steady state and transient dynamical properties of the population growth model subjected to the cross correlated sine wiener bounded noise are investigated based on the approximate fokker planck equation the analytical expressions of stationary probability distribution function and extinction time of the population growth model are obtained the results show that the bounded noise intensity and the cross correlated bounded noise intensity can cause the population extinction in particular the population density are sensitive to the environmental fluctuation multiplicative noise and the self correlated time on the other hand the effects of noise intensities on the extinction time are discussed the results indicate that the extinction time can be decreased with the intrinsic stochasticity additive noise and the self correlated time increasing for the positive correlated case the result is similar to the additive noise and the self correlated time however for the negative correlated case the extinction time can be increased with the intensity increasing in particular the environmental fluctuation multiplicative noise causes noise enhanced stability for the negative correlated case and the extinction time firstly decreases for the small environmental fluctuation and then increases for the large environmental fluctuation for the positive correlated case the numerical results are in basic agreement with the theoretical predictions

bistable kinetic model driven by correlated noises steady state analysis a simple rule to obtain the fokker planck equation for a general one dimensional system driven by correlated gaussian white noises is proved by the functional method the fokker planck equation obtained in this paper is applied to the bistable kinetic model we find the following for the steady state 1 in the ensuremath alpha d parameter plane ensuremath alpha is the strength of the additive noise and d is the multiplicative noise strength the critical curve separating the unimodal and bimodal regions of the stationary probability distribution spd of the model is shown to be affected by ensuremath lambda the degree of correlation of the noises as ensuremath lambda is increased the area of the bimodal region in the ensuremath alpha d plane is contracted 2 when we take a point fixed in the ensuremath alpha d plane and increase ensuremath lambda the form of the spd changes from a bimodal to a unimodal structure 3 the positions of the extreme value of the spd of the model sensitively depend on the strength of the multiplicative noise and weakly depend on the additive noise strength 4 for ensuremath lambda 1 the case of perfectly correlated noises when the parameters ensuremath alpha and d take values in the neighborhood of the line ensuremath alpha d in the ensuremath alpha d plane the spd s corresponding to the points ensuremath alpha dg1 and ensuremath alpha d1 exhibit a very different shape of divergence therefore the ratio ensuremath alpha d 1 plays the role of a critical ratio

enhancement of stochastic resonance the role of non gaussian noises abstract we have analyzed the phenomenon of stochastic resonance in a double well potential driven by a colored non gaussian noise using a path integral approach we have obtained a consistent markovian approximation that enables us to get through the two state theory analytical expressions for the signal to noise ratio finding an enhancement of this quantity when the system departs from gaussian behavior this finding is supported by extensive numerical simulations we discuss the relation of these results to some experiments in sensory systems

transitions induced by bounded noise abstract we study noise induced transitions when the system is driven separately by a gaussian colored noise and a non gaussian bounded noise with the same mean and stationary correlation clear differences in the nature of such transitions for these two cases are observed these transitions are shown to be enhanced essentially by the bounded noisy driving the results are obtained through a new approach based on numerical analysis of hyperbolic systems

the effects of time delay on stochastic resonance in a bistable system with correlated noises the effects of time delay on stochastic resonance sr in a bistable system with time delay correlated noises and periodic signal are studied by using the theory of signal to noise ratio snr the expression of the snr is derived under the adiabatic limit and the small delay time approximation it is found that i for the case of no correlations between multiplicative and additive noise the delay time can enhance the snr as a function of the multiplicative noise intensity and it can restrain the snr as a function of the additive noise intensity d ii for the case of correlations between multiplicative and additive noise can induce a minimum and maximum in curve of the snr as a function of and can intensively restrain the snr as a function of the d and there is a critical value of delay tim c 0 1 in the height of the snr peak with change of i e when takes value blow c t he boosts up the snr as a function of the strength of correlations between multiplicative and additive noise however when takes value above c t he restrains that

transitions induced by cross correlated bounded noises and time delay in a genotype selection model abstract we present a numerical investigation of occurrence of transitions in a genotype selection model with time delay where two bounded noises are cross correlated stationary probability distribution spd function is obtained it is found that the multiplicative bounded noise can facilitate the gene separation and it plays a constructive role in the genetic selection progress while the additive bounded noise suppresses the gene separation the strong correlation between noises gives a big chance to one type haploid out of the group besides what is more interesting is that the correlation time can induce a new transitions i e the curve of the spd changes from unimodal to bimodal and then to four peaks as the correlation time increases

properties of principal component methods for functional and longitudinal data analysis the use of principal component methods to analyze functional data is appropriate in a wide range of different settings in studies of functional data analysis it has often been assumed that a sample of random functions is observed precisely in the continuum and without noise while this has been the traditional setting for functional data analysis in the context of longitudinal data analysis a random function typically represents a patient or subject who is observed at only a small number of randomly distributed points with nonnegligible measurement error nevertheless essentially the same methods can be used in both these cases as well as in the vast number of settings that lie between them how is performance affected by the sampling plan in this paper we answer that question we show that if there is a sample of n functions or subjects then estimation of eigenvalues is a semiparametric problem with root n consistent estimators even if only a few observations are made of each function and if each observation is encumbered by noise however estimation of eigenfunctions becomes a nonparametric problem when observations are sparse the optimal convergence rates in this case are those which pertain to more familiar function estimation settings we also describe the effects of sampling at regularly spaced points as opposed to random points in particular it is shown that there are often advantages in sampling randomly however even in the case of noisy data there is a threshold sampling rate depending on the number of functions treated above which the rate of sampling either randomly or regularly has negligible impact on estimator performance no matter whether eigenfunctions or eigenvectors are being estimated

unified empirical likelihood ratio tests for functional concurrent linear models and the phase transition from sparse to dense functional data summary we consider the problem of testing functional constraints in a class of functional concurrent linear models where both the predictors and the response are functional data measured at discrete time points we propose test procedures based on the empirical likelihood with bias corrected estimating equations to conduct both pointwise and simultaneous inferences the asymptotic distributions of the test statistics are derived under the null and local alternative hypotheses where sparse and dense functional data are considered in a unified framework we find a phase transition in the asymptotic null distributions and the orders of detectable alternatives from sparse to dense functional data specifically the tests proposed can detect alternatives of n order when the number of repeated measurements per curve is of an order larger than n 0 with n being the number of curves the transition points 0 for pointwise and simultaneous tests are different and both are smaller than the transition point in the estimation problem simulation studies and real data analyses are conducted to demonstrate the methods proposed

functional linear regression analysis for longitudinal data we propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data where both the predictor and response are functions of a covariate such as time predictor and response processes have smooth random trajectories and the data consist of a small number of noisy repeated measurements made at irregular times for a sample of subjects in longitudinal studies the number of repeated measurements per subject is often small and may be modeled as a discrete random number and accordingly only a finite and asymptotically nonincreasing number of measurements are available for each subject or experimental unit we propose a functional regression approach for this situation using functional principal component analysis where we estimate the functional principal component scores through conditional expectations this allows the prediction of an unobserved response trajectory from sparse measurements of a predictor trajectory the resulting technique is flexible and allows for different patterns regarding the timing of the measurements obtained for predictor and response trajectories asymptotic properties for a sample of n subjects are investigated under mild conditions as n oo and we obtain consistent estimation for the regression function besides convergence results for the components of functional linear regression such as the regression parameter function we construct asymptotic pointwise confidence bands for the predicted trajectories a functional coefficient of determination as a measure of the variance explained by the functional regression model is introduced extending the standard r 2 to the functional case the proposed methods are illustrated with a simulation study longitudinal primary biliary liver cirrhosis data and an analysis of the longitudinal relationship between blood pressure and body mass index

effective markovian approximation for non gaussian noises a path integral approach abstract we have analyzed diffusion in a double well potential driven by a colored non gaussian noise using a path integral approach we have obtained a consistent markovian approximation to the initially non markovian problem such an approximation allows us to get analytical expressions for the mean first passage time that has been tested against extensive numerical simulations

internal fluctuations period doubling and chemical chaos the effects of internal molecular fluctuations on period doubling bifurcations and chaotic band merging are studied in a well stirred reactive chemical system where spatial degrees of freedom play no role the calculations are carried out using a stochastic model based on reactive lattice gas cellular automata molecular fluctuations arising from reactive collisions are incorporated in the model and the mass action rate equations are recovered in the mean field limit large system sizes can be studied and permit the investigation of the internal noise scaling structure in the periodic and chaotic regimes for the willamowski r ossler system the noise scaling exponent is consistent with that found for one dimensional quadratic maps subject to external noise the model provides a means to study small non equilibrium reacting systems where fluctuations may not be neglected

bounded noise induced transitions in a tumor immune system interplay by studying a recent biophysical model of tumor growth in the presence of the immune system here we propose that the phenomenon of evasion of tumors from immune control at a temporal mesoscale might in some cases be due to random fluctuations in the levels of the immune system bounded noises are considered but the gaussian approach is also used for analytical reference after showing that in the case of bounded noises there may be multiple attractors in the space of probability densities we numerically show that the velocity of convergence toward asymptotic density is very slow and that a transitory analysis is needed then by simulations using the sine wiener and the tsallis noises we show that if the level of the noise is sufficiently large then there may be the onset of noise induced transitions in the transitory density evaluated at realistic times namely the transitions are from unimodal density centered at low values of tumor burden to bimodal densities that have a second maximum centered at higher values however those transitions depend on the distribution of the noise

unified inference for sparse and dense longitudinal models in longitudinal data analysis statistical inference for sparse data and dense data could be substantially different for kernel smoothing estimate of the mean function the convergence rates and limiting variance functions are different under the two scenarios the latter phenomenon poses challenges for statistical inference as a subjective choice between the sparse and dense cases may lead to wrong conclusions we develop self normalization based methods that can adapt to the sparse and dense cases in a unified framework simulations show that the proposed methods outperform some existing methods

effect of non gaussian noises on the stochastic resonance like phenomenon in gated traps we exploit a simple one dimensional trapping model introduced before prompted by the problem of ion current across a biological membrane the voltage sensitive channels are open or closed depending on the value taken by an external potential that has two contributions a deterministic periodic and a stochastic one here we assume that the noise source is colored and non gaussian with a q dependent probability distribution where q is a parameter indicating the departure from gaussianity we analyze the behavior of the oscillation amplitude as a function of both q and the noise correlation time the main result is that in addition to the resonant like maximum as a function of the noise intensity there is a new resonant maximum as a function of the parameter q

numerical computation of 3d heat transfer in complex parallel heat exchangers using generalized graetz modes we propose and develop a variational formulation dedicated to the simulation of parallel convective heat exchangers that handles possibly complex input output conditions as well as connection between pipes it is based on a spectral method that allows to re cast three dimensional heat exchangers into a two dimensional eigenvalue problem named the generalized graetz problem our formulation handles either convective adiabatic or prescribed temperature at the entrance or at the exit of the exchanger this formulation is robust to mode truncation offering a huge reduction in computational cost and providing insights into the most contributing structure to exchanges and transfers several examples of heat exchangers are analyzed their numerical convergence is tested and the numerical efficiency of the approach is illustrated in the case of poiseuille flow in tubes

stochastic resonance in a tumor immune system subject to bounded noises and time delay abstract immunotherapy is one of the most recent approaches in cancer therapy a mathematical model of tumor immune interaction subject to a periodic immunotherapy treatment imitated by a periodic signal correlative and bounded stochastic fluctuations and time delays is investigated by numerical simulations for its signal power amplification spa within the tailored parameter regime the synchronous response of tumor growth to the immunotherapy stochastic resonance sr versus both the noises and delays is obtained the details are as follows i the peak values of spa versus the noise intensity a in the proliferation term of tumor cells decrease as the frequency of periodic signal increases i e an increase of the frequency restrains the sr ii an increase of the amplitude of periodic signal restrains the sr versus a but boosts up the sr versus the noise intensity b in the immune term iii there is an optimum cross correlated degree between the two bounded noises at which the system exhibits the strongest sr versus the delay time the reaction time of tumor cell population to their surrounding environment constraints iv upon increasing the delay time double sr versus the delay time the time taken by both the tumor antigen identification and tumor stimulated proliferation of effectors emerges these results may be helpful for an immunotherapy treatment for the sufferer

semiparametric gee analysis in partially linear single index models for longitudinal data in this article we study a partially linear single index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases a semiparametric estimation method based on a combination of the local linear smoothing and generalized estimation equations gee is introduced to estimate the two parameter vectors as well as the unknown link function under some mild conditions we derive the asymptotic properties of the proposed parametric and nonparametric estimators in different scenarios from which we find that the convergence rates and asymptotic variances of the proposed estimators for sparse longitudinal data would be substantially different from those for dense longitudinal data we also discuss the estimation of the covariance or weight matrices involved in the semiparametric gee method furthermore we provide some numerical studies including monte carlo simulation and an empirical application to illustrate our methodology and theory

transitions induced by time delays and cross correlated sine wiener noises in a tumor immune system interplay abstract a model of tumor immune system interplay with time delays and cross correlated sine wiener noises is investigated by numerical simulations for the stationary probability distribution spd and stationary mean value x s t of tumor cell population results show that i the structure of the spd transfers from bimodal to unimodal as the reaction time of the cell population to environmental constraints increases ii conversely as the time of immune system to develop specific immune competence and the correlation time of noises increase the structure of the spd transfers from unimodal to bimodal iii as the cross correlated intensity between noises increases the transitions induced by are suppressed

delay fokker planck equations novikov s theorem and boltzmann distributions as small delay approximations we study time delayed stochastic systems that can be described by means of so called delay fokker planck equations using novikov s theorem we first show that the theory of delay fokker planck equations is on an equal footing with the theory of ordinary fokker planck equations subsequently we derive stationary distributions in the case of small time delays in the case of additive noise systems these distributions can be cast into the form of boltzmann distributions involving effective potential functions

the properties of the anti tumor model with coupling non gaussian noise and gaussian colored noise abstract the anti tumor model with correlation between multiplicative non gaussian noise and additive gaussian colored noise has been investigated in this paper the behaviors of the stationary probability distribution demonstrate that the multiplicative non gaussian noise plays a dual role in the development of tumor and an appropriate additive gaussian colored noise can lead to a minimum of the mean value of tumor cell population the mean first passage time is calculated to quantify the effects of noises on the transition time of tumors between the stable states an increase in both the non gaussian noise intensity and the departure from the gaussian noise can accelerate the transition from the disease state to the healthy state on the contrary an increase in cross correlated degree will slow down the transition moreover the correlation time can enhance the stability of the disease state

statistical inferences for functional data with modem technology development functional data are being observed frequently in many scientific fields a popular method for analyzing such functional data is smoothing first then estimation that is statistical inference such as estimation and hypothesis testing about functional data is conducted based on the substitution of the underlying individual functions by their reconstructions obtained by one smoothing technique or another however little is known about this substitution effect on functional data analysis in this paper this problem is investigated when the local polynomial kernel lpk smoothing technique is used for individual function reconstructions we find that under some mild conditions the substitution effect can be ignored asymptotically based on this we construct lpk reconstruction based estimators for the mean covariance and noise variance functions of a functional data set and derive their asymptotics we also propose a gcv rule for selecting good bandwidths for the lpk reconstructions when the mean function also depends on some time independent covariates we consider a functional linear model where the mean function is linearly related to the covariates but the covariate effects are functions of time the lpk reconstruction based estimators for the covariate effects and the covariance function are also constructed and their asymptotics are derived moreover we propose a l 2 norm based global test statistic for a general hypothesis testing problem about the covariate effects and derive its asymptotic random expression the effect of the bandwidths selected by the proposed gcv rule on the accuracy of the lpk reconstructions and the mean function estimator is investigated via a simulation study the proposed methodologies are illustrated via an application to a real functional data set collected in climatology

the generalized graetz problem in finite domains we consider the generalized graetz problem associated with stationary convection diffusion inside a domain having any regular three dimensional translationally invariant section and finite or semi infinite extent our framework encompasses any previous extended and conjugated graetz generalizations and provides theoretical bases for computing the orthogonal set of generalized two dimensional graetz modes the theoretical framework includes both heterogeneous and possibly anisotropic diffusion tensors in the case of semi infinite domains the existence of a bounded solution is shown from the analysis of two dimensional operator eigenvectors which form a basis of l 2 in the case of finite domains a similar basis can be exhibited and the mode s amplitudes can be obtained from the inversion of newly defined finite domain operator our analysis includes both the theoretical and practical issues associated with this finite domain operator inversion as well as its interpretation as a multireflection ima

stochastic model for tumor growth with immunization we analyze a stochastic model for tumor cell growth with both multiplicative and additive colored noises as well as nonzero cross correlations in between whereas the death rate within the logistic model is altered by a deterministic term characterizing immunization the birth rate is assumed to be stochastically changed due to biological motivated growth processes leading to a multiplicative internal noise moreover the system is subjected to an external additive noise which mimics the influence of the environment of the tumor the stationary probability distribution p s is derived depending on the finite correlation time the immunization rate and the strength of the cross correlation p s offers a maximum which becomes more pronounced for increasing immunization rate the mean first passage time is also calculated in order to find out under which conditions the tumor can suffer extinction its characteristics are again controlled by the degree of immunization and the strength of the cross correlation the behavior observed can be interpreted in terms of a biological model of tumor evolution

delay fokker planck equations perturbation theory and data analysis for nonlinear stochastic systems with time delays we study nonlinear stochastic systems with time delayed feedback using the concept of delay fokker planck equations introduced by guillouzic l heureux and longtin we derive an analytical expression for stationary distributions using first order perturbation theory we demonstrate how to determine drift functions and noise amplitudes of this kind of systems from experimental data in addition we show that the fokker planck perspective for stochastic systems with time delays is consistent with the so called extended phase space approach to time delayed systems

stochastic resonance induced by bounded noise and periodic signal in an asymmetric bistable system abstract stochastic resonance sr induced by the bounded noise in an asymmetric bistable system is investigated based on stochastic simulation the signal power amplification s p a is derived for the case of the additive modulated signal the simulation results indicate that 1 the sr phenomenon is weakened by the asymmetry of the system 2 increasing the frequency restricts the sr of the system 3 there is an optimum value of amplitude a matching the strongest sr phenomenon