A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decompo...A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.展开更多
Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. Th...Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. The minimum negative log-likelihood function was used as the objective function to optimize instead of using iterative method to solve complex system of equations,and the problem of parameter estimation of improved NHPP model was solved by immune clone algorithm. And the interval estimation of reliability indices was given by using fisher information matrix method and delta method. An example of failure truncated data from multiple numerical control( NC) machine tools was taken to prove the method. and the results show that the algorithm has a higher convergence rate and computational accuracy, which demonstrates the feasibility of the method.展开更多
Based on the major gene and polygene mixed inheritance model for multiple correlated quantitative traits, the authors proposed a new joint segregation analysis method of major gene controlling multiple correlated quan...Based on the major gene and polygene mixed inheritance model for multiple correlated quantitative traits, the authors proposed a new joint segregation analysis method of major gene controlling multiple correlated quantitative traits, which include major gene detection and its effect and variation estimation. The effect and variation of major gene are estimated by the maximum likelihood method implemented via expectation-maximization （EM） algorithm. Major gene is tested with the likelihood ratio （LR） test statistic. Extensive simulation studies showed that joint analysis not only increases the statistical power of major gene detection but also improves the precision and accuracy of major gene effect estimates. An example of the plant height and the number of tiller of F2 population in rice cross Duonieai x Zhonghua 11 was used in the illustration. The results indicated that the genetic difference of these two traits in this cross refers to only one pleiotropic major gene. The additive effect and dominance effect of the major gene are estimated as -21.3 and 40.6 cm on plant height, and 22.7 and -25.3 on number of tiller, respectively. The major gene shows overdominance for plant height and close to complete dominance for number of tillers.展开更多
Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcom...Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcomes. This paper focuses on the maximum likelihood estimation of joint location and scale models of the skew-normal distribution. The proposed procedure can simultaneously estimate parameters in the location model and the scale model. Simulation studies and a real example are used to illustrate the proposed methodologies.展开更多
Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades...Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models： a skew-normal mixture of joint location, scale and skewness models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation-Maximization （EM） algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index （BMI） data are presented.展开更多
An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fix...An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fixed initial radius, the ILSD adopts an adaptive radius to accelerate the list cdnstruction. Characterized by low-complexity and radius-insensitivity, the proposed algorithm makes iterative joint detection and decoding more realizable in multiple-antenna systems. Simulation results show that computational savings of ILSD over LSD are more apparent with more transmit antennas or larger constellations, and with no performance degradation. Because the complexity of the ILSD algorithm almost keeps invariant with the increasing of initial radius, the BER performance can be improved by selecting a sufficiently large radius.展开更多
Multiple myeloma (MM) is a type of cancer that remains incurable. In the last decade, most research into MM has focused on investigating the improvement in the therapeutic strategy. Our study assesses the survival pro...Multiple myeloma (MM) is a type of cancer that remains incurable. In the last decade, most research into MM has focused on investigating the improvement in the therapeutic strategy. Our study assesses the survival probability of 48 patients diagnosed with MM based on parametric and non-parametric techniques. We performed parametric survival analysis and found a well-def- ined probability distribution of the survival time to follow three-parameter lognormal. We then estimated the survival probability and compared it with the commonly used non-parametric Kaplan-Meier survival analysis of the survival times. The comparison of the survival probability estimates of the two methods revealed a better survival probability estimate by the parametric method than the Kaplan-Meier. The parametric survival analysis is more robust and efficient because it is based on a well-defined parametric probabilistic distribution, hence preferred over the non-parametric Kaplan-Meier. This study offers therapeutic significance for further enhancement in the treatment strategy of multiple myeloma cancer.展开更多
The problem of estimating the carrier frequency offsets in Multiple-Input Multiple-Output (MIMO) systems with distributed transmit antennas is addressed. It is supposed that the transmit antennas are distributed while...The problem of estimating the carrier frequency offsets in Multiple-Input Multiple-Output (MIMO) systems with distributed transmit antennas is addressed. It is supposed that the transmit antennas are distributed while the receive antennas are still centralized, and the general case where both the time delays and the frequency offsets are possibly different for each transmit antenna is considered. The channel is supposed to be frequency flat, and the macroscopic fading is also taken into consideration. A carrier frequency offset estimator based on Maximum Likelihood (ML) is proposed, which can separately estimate the frequency offset for each transmit antenna and exploit the spatial diversity. The Cramer-Rao Bound (CRB) for synchronous MIMO (i.e., the time delays for each transmit antenna are all equal) is also derived. Simulation results are given to illustrate the per- formance of the estimator and compare it with the CRB. It is shown that the estimator can provide satisfactory frequency offset estimates and its performance is close to the CRB for the Signal-to-Noise Ratio (SNR) below 20dB.展开更多
文摘A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.
基金National CNC Special Project,China(No.2010ZX04001-032)the Youth Science and Technology Foundation of Gansu Province,China(No.145RJYA307)
文摘Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. The minimum negative log-likelihood function was used as the objective function to optimize instead of using iterative method to solve complex system of equations,and the problem of parameter estimation of improved NHPP model was solved by immune clone algorithm. And the interval estimation of reliability indices was given by using fisher information matrix method and delta method. An example of failure truncated data from multiple numerical control( NC) machine tools was taken to prove the method. and the results show that the algorithm has a higher convergence rate and computational accuracy, which demonstrates the feasibility of the method.
基金This research was supported by the National Natural Science Foundation of China to Xu Chenwu （39900080, 30270724 and 30370758）.
文摘Based on the major gene and polygene mixed inheritance model for multiple correlated quantitative traits, the authors proposed a new joint segregation analysis method of major gene controlling multiple correlated quantitative traits, which include major gene detection and its effect and variation estimation. The effect and variation of major gene are estimated by the maximum likelihood method implemented via expectation-maximization （EM） algorithm. Major gene is tested with the likelihood ratio （LR） test statistic. Extensive simulation studies showed that joint analysis not only increases the statistical power of major gene detection but also improves the precision and accuracy of major gene effect estimates. An example of the plant height and the number of tiller of F2 population in rice cross Duonieai x Zhonghua 11 was used in the illustration. The results indicated that the genetic difference of these two traits in this cross refers to only one pleiotropic major gene. The additive effect and dominance effect of the major gene are estimated as -21.3 and 40.6 cm on plant height, and 22.7 and -25.3 on number of tiller, respectively. The major gene shows overdominance for plant height and close to complete dominance for number of tillers.
基金Supported by the National Natural Science Foundation of China(11261025,11201412)the Natural Science Foundation of Yunnan Province(2011FB016)the Program for Middle-aged Backbone Teacher,Yunnan University
文摘Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcomes. This paper focuses on the maximum likelihood estimation of joint location and scale models of the skew-normal distribution. The proposed procedure can simultaneously estimate parameters in the location model and the scale model. Simulation studies and a real example are used to illustrate the proposed methodologies.
基金Supported by the National Natural Science Foundation of China(11261025,11561075)the Natural Science Foundation of Yunnan Province(2016FB005)the Program for Middle-aged Backbone Teacher,Yunnan University
文摘Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models： a skew-normal mixture of joint location, scale and skewness models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation-Maximization （EM） algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index （BMI） data are presented.
基金The National Natural Science Founda-tion of China ( No 60496316)the National Hi-Tech Re-search and Development Program (863) of China (No2006-AA01Z270)
文摘An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fixed initial radius, the ILSD adopts an adaptive radius to accelerate the list cdnstruction. Characterized by low-complexity and radius-insensitivity, the proposed algorithm makes iterative joint detection and decoding more realizable in multiple-antenna systems. Simulation results show that computational savings of ILSD over LSD are more apparent with more transmit antennas or larger constellations, and with no performance degradation. Because the complexity of the ILSD algorithm almost keeps invariant with the increasing of initial radius, the BER performance can be improved by selecting a sufficiently large radius.
文摘Multiple myeloma (MM) is a type of cancer that remains incurable. In the last decade, most research into MM has focused on investigating the improvement in the therapeutic strategy. Our study assesses the survival probability of 48 patients diagnosed with MM based on parametric and non-parametric techniques. We performed parametric survival analysis and found a well-def- ined probability distribution of the survival time to follow three-parameter lognormal. We then estimated the survival probability and compared it with the commonly used non-parametric Kaplan-Meier survival analysis of the survival times. The comparison of the survival probability estimates of the two methods revealed a better survival probability estimate by the parametric method than the Kaplan-Meier. The parametric survival analysis is more robust and efficient because it is based on a well-defined parametric probabilistic distribution, hence preferred over the non-parametric Kaplan-Meier. This study offers therapeutic significance for further enhancement in the treatment strategy of multiple myeloma cancer.
基金the National Natural Science Foundation of China (No. 60272009, No. 60572090, No. 60472045, No. 60496313 and No. 60602009).
文摘The problem of estimating the carrier frequency offsets in Multiple-Input Multiple-Output (MIMO) systems with distributed transmit antennas is addressed. It is supposed that the transmit antennas are distributed while the receive antennas are still centralized, and the general case where both the time delays and the frequency offsets are possibly different for each transmit antenna is considered. The channel is supposed to be frequency flat, and the macroscopic fading is also taken into consideration. A carrier frequency offset estimator based on Maximum Likelihood (ML) is proposed, which can separately estimate the frequency offset for each transmit antenna and exploit the spatial diversity. The Cramer-Rao Bound (CRB) for synchronous MIMO (i.e., the time delays for each transmit antenna are all equal) is also derived. Simulation results are given to illustrate the per- formance of the estimator and compare it with the CRB. It is shown that the estimator can provide satisfactory frequency offset estimates and its performance is close to the CRB for the Signal-to-Noise Ratio (SNR) below 20dB.
