Empirical Likelihood Inference for Functional Coefficient ARCH-M Model View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02

AUTHORS

Hai Qing Zhao, Yuan Li, Yan Meng Zhao

ABSTRACT

Empirical likelihood inference for parametric and nonparametric parts in functional coefficient ARCH-M models is investigated in this paper. Firstly, the kernel smoothing technique is used to estimate coefficient function δ(x). In this way we obtain an estimated function with parameter β. Secondly, the empirical likelihood method is developed to estimate the parameter β. An estimated empirical log-likelohood ratio is proved to be asymptotically standard chi-squred, and the maximum empirical likelihood estimation (MELE) for β is shown to be asymptotically normal. Finally, based on the MELE of β, the empirical likelihood approach is again applied to reestimate the nonparametric part δ(x). The empirical log-likelohood ratio for δ(x) is proved to be also asymptotically standard chi-squred. Simulation study shows that the proposed method works better than the normal approximation method in terms of average areas of confidence regions for β, and the empirical likelihood confidence belt for δ(x) performs well. More... »

PAGES

1-27

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10114-018-8083-9

DOI

http://dx.doi.org/10.1007/s10114-018-8083-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110668340


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Lingnan Normal University", 
          "id": "https://www.grid.ac/institutes/grid.469319.0", 
          "name": [
            "School of Mathematics and Information Science, Guangzhou University, 510006, Guangzhou, P. R. China", 
            "School of Mathematics and Statistics, Lingnan Normal University, 524048, Zhanjiang, P. R. China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhao", 
        "givenName": "Hai Qing", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Guangzhou University", 
          "id": "https://www.grid.ac/institutes/grid.411863.9", 
          "name": [
            "School of Economics and Statistics, Guangzhou University, 510006, Guangzhou, P. R. China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Yuan", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shenzhen University", 
          "id": "https://www.grid.ac/institutes/grid.263488.3", 
          "name": [
            "School of Mathematics and Statistics, Shenzhen University, 518060, Shenzhen, P. R. China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhao", 
        "givenName": "Yan Meng", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2004.00432.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001669517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-7152(98)00230-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017562467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jeconom.2011.09.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024924527"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0165-1765(91)90120-a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025622538"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.0143-9782.2003.01771.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032681089"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2014/189426", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034104208"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10114-008-6434-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038545968", 
          "https://doi.org/10.1007/s10114-008-6434-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10114-008-6434-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038545968", 
          "https://doi.org/10.1007/s10114-008-6434-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.econlet.2005.07.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040068470"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.econlet.2003.05.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041144211"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-4076(92)90070-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042156626"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10114-011-9157-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042290395", 
          "https://doi.org/10.1007/s10114-011-9157-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10255-014-0410-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042692086", 
          "https://doi.org/10.1007/s10255-014-0410-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0266466606060208", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043143586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-69395-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044828272", 
          "https://doi.org/10.1007/978-0-387-69395-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1044828272", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11425-016-5151-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052020401", 
          "https://doi.org/10.1007/s11425-016-5151-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-405x(89)90049-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052314872"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/75.2.237", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059419808"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/073500103288618954", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064198990"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/106186008x321068", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064199635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aop/1176997023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064405770"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1069362388", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064406499"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1176325370", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064406671"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1913242", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069640603"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-02", 
    "datePublishedReg": "2019-02-01", 
    "description": "Empirical likelihood inference for parametric and nonparametric parts in functional coefficient ARCH-M models is investigated in this paper. Firstly, the kernel smoothing technique is used to estimate coefficient function \u03b4(x). In this way we obtain an estimated function with parameter \u03b2. Secondly, the empirical likelihood method is developed to estimate the parameter \u03b2. An estimated empirical log-likelohood ratio is proved to be asymptotically standard chi-squred, and the maximum empirical likelihood estimation (MELE) for \u03b2 is shown to be asymptotically normal. Finally, based on the MELE of \u03b2, the empirical likelihood approach is again applied to reestimate the nonparametric part \u03b4(x). The empirical log-likelohood ratio for \u03b4(x) is proved to be also asymptotically standard chi-squred. Simulation study shows that the proposed method works better than the normal approximation method in terms of average areas of confidence regions for \u03b2, and the empirical likelihood confidence belt for \u03b4(x) performs well.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10114-018-8083-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1040372", 
        "issn": [
          "1439-8516", 
          "1439-7617"
        ], 
        "name": "Acta Mathematica Sinica, English Series", 
        "type": "Periodical"
      }
    ], 
    "name": "Empirical Likelihood Inference for Functional Coefficient ARCH-M Model", 
    "pagination": "1-27", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "615553c91563d3d0a6f40c5dc4c3f2525158388fcf0fb681da1d61454304ef6a"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10114-018-8083-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1110668340"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10114-018-8083-9", 
      "https://app.dimensions.ai/details/publication/pub.1110668340"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T08:24", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000296_0000000296/records_57231_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10114-018-8083-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10114-018-8083-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10114-018-8083-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10114-018-8083-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10114-018-8083-9'


 

This table displays all metadata directly associated to this object as RDF triples.

149 TRIPLES      21 PREDICATES      49 URIs      17 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10114-018-8083-9 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N28ac23ba604644269d8410a1088764ce
4 schema:citation sg:pub.10.1007/978-0-387-69395-8
5 sg:pub.10.1007/s10114-008-6434-7
6 sg:pub.10.1007/s10114-011-9157-0
7 sg:pub.10.1007/s10255-014-0410-z
8 sg:pub.10.1007/s11425-016-5151-4
9 https://app.dimensions.ai/details/publication/pub.1044828272
10 https://doi.org/10.1016/0165-1765(91)90120-a
11 https://doi.org/10.1016/0304-405x(89)90049-4
12 https://doi.org/10.1016/0304-4076(92)90070-8
13 https://doi.org/10.1016/j.econlet.2003.05.003
14 https://doi.org/10.1016/j.econlet.2005.07.001
15 https://doi.org/10.1016/j.jeconom.2011.09.028
16 https://doi.org/10.1016/s0167-7152(98)00230-2
17 https://doi.org/10.1017/s0266466606060208
18 https://doi.org/10.1046/j.0143-9782.2003.01771.x
19 https://doi.org/10.1093/biomet/75.2.237
20 https://doi.org/10.1111/j.1467-9868.2004.00432.x
21 https://doi.org/10.1155/2014/189426
22 https://doi.org/10.1198/073500103288618954
23 https://doi.org/10.1198/106186008x321068
24 https://doi.org/10.1214/aop/1176997023
25 https://doi.org/10.1214/aos/1069362388
26 https://doi.org/10.1214/aos/1176325370
27 https://doi.org/10.2307/1913242
28 schema:datePublished 2019-02
29 schema:datePublishedReg 2019-02-01
30 schema:description Empirical likelihood inference for parametric and nonparametric parts in functional coefficient ARCH-M models is investigated in this paper. Firstly, the kernel smoothing technique is used to estimate coefficient function δ(x). In this way we obtain an estimated function with parameter β. Secondly, the empirical likelihood method is developed to estimate the parameter β. An estimated empirical log-likelohood ratio is proved to be asymptotically standard chi-squred, and the maximum empirical likelihood estimation (MELE) for β is shown to be asymptotically normal. Finally, based on the MELE of β, the empirical likelihood approach is again applied to reestimate the nonparametric part δ(x). The empirical log-likelohood ratio for δ(x) is proved to be also asymptotically standard chi-squred. Simulation study shows that the proposed method works better than the normal approximation method in terms of average areas of confidence regions for β, and the empirical likelihood confidence belt for δ(x) performs well.
31 schema:genre research_article
32 schema:inLanguage en
33 schema:isAccessibleForFree false
34 schema:isPartOf sg:journal.1040372
35 schema:name Empirical Likelihood Inference for Functional Coefficient ARCH-M Model
36 schema:pagination 1-27
37 schema:productId N010698d92ae2499ba3a178dd5f0313fb
38 N21342576bbce4a499df916110ad473f1
39 Nef1ebcb1b0ff48e3b62fd5a0a1237bb0
40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110668340
41 https://doi.org/10.1007/s10114-018-8083-9
42 schema:sdDatePublished 2019-04-11T08:24
43 schema:sdLicense https://scigraph.springernature.com/explorer/license/
44 schema:sdPublisher N0359d6a647674865bdb6c10ee6df5ff0
45 schema:url https://link.springer.com/10.1007%2Fs10114-018-8083-9
46 sgo:license sg:explorer/license/
47 sgo:sdDataset articles
48 rdf:type schema:ScholarlyArticle
49 N010698d92ae2499ba3a178dd5f0313fb schema:name readcube_id
50 schema:value 615553c91563d3d0a6f40c5dc4c3f2525158388fcf0fb681da1d61454304ef6a
51 rdf:type schema:PropertyValue
52 N0359d6a647674865bdb6c10ee6df5ff0 schema:name Springer Nature - SN SciGraph project
53 rdf:type schema:Organization
54 N13332c66fa30431ebba6bc1db15e7014 rdf:first Nbba80439bbbc45d8987b49561fee2240
55 rdf:rest rdf:nil
56 N17ceb633d7104ff1aec07ab05ad4c3d3 schema:affiliation https://www.grid.ac/institutes/grid.411863.9
57 schema:familyName Li
58 schema:givenName Yuan
59 rdf:type schema:Person
60 N21342576bbce4a499df916110ad473f1 schema:name dimensions_id
61 schema:value pub.1110668340
62 rdf:type schema:PropertyValue
63 N28ac23ba604644269d8410a1088764ce rdf:first Ne323e40980504353afa86d7cf6a23ab4
64 rdf:rest N65affbda00c947849c0a4ea36f213bad
65 N65affbda00c947849c0a4ea36f213bad rdf:first N17ceb633d7104ff1aec07ab05ad4c3d3
66 rdf:rest N13332c66fa30431ebba6bc1db15e7014
67 Nbba80439bbbc45d8987b49561fee2240 schema:affiliation https://www.grid.ac/institutes/grid.263488.3
68 schema:familyName Zhao
69 schema:givenName Yan Meng
70 rdf:type schema:Person
71 Ne323e40980504353afa86d7cf6a23ab4 schema:affiliation https://www.grid.ac/institutes/grid.469319.0
72 schema:familyName Zhao
73 schema:givenName Hai Qing
74 rdf:type schema:Person
75 Nef1ebcb1b0ff48e3b62fd5a0a1237bb0 schema:name doi
76 schema:value 10.1007/s10114-018-8083-9
77 rdf:type schema:PropertyValue
78 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
79 schema:name Mathematical Sciences
80 rdf:type schema:DefinedTerm
81 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
82 schema:name Statistics
83 rdf:type schema:DefinedTerm
84 sg:journal.1040372 schema:issn 1439-7617
85 1439-8516
86 schema:name Acta Mathematica Sinica, English Series
87 rdf:type schema:Periodical
88 sg:pub.10.1007/978-0-387-69395-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044828272
89 https://doi.org/10.1007/978-0-387-69395-8
90 rdf:type schema:CreativeWork
91 sg:pub.10.1007/s10114-008-6434-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038545968
92 https://doi.org/10.1007/s10114-008-6434-7
93 rdf:type schema:CreativeWork
94 sg:pub.10.1007/s10114-011-9157-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042290395
95 https://doi.org/10.1007/s10114-011-9157-0
96 rdf:type schema:CreativeWork
97 sg:pub.10.1007/s10255-014-0410-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1042692086
98 https://doi.org/10.1007/s10255-014-0410-z
99 rdf:type schema:CreativeWork
100 sg:pub.10.1007/s11425-016-5151-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052020401
101 https://doi.org/10.1007/s11425-016-5151-4
102 rdf:type schema:CreativeWork
103 https://app.dimensions.ai/details/publication/pub.1044828272 schema:CreativeWork
104 https://doi.org/10.1016/0165-1765(91)90120-a schema:sameAs https://app.dimensions.ai/details/publication/pub.1025622538
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1016/0304-405x(89)90049-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052314872
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/0304-4076(92)90070-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042156626
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1016/j.econlet.2003.05.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041144211
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/j.econlet.2005.07.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040068470
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/j.jeconom.2011.09.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024924527
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/s0167-7152(98)00230-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017562467
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1017/s0266466606060208 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043143586
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1046/j.0143-9782.2003.01771.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1032681089
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1093/biomet/75.2.237 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059419808
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1111/j.1467-9868.2004.00432.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1001669517
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1155/2014/189426 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034104208
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1198/073500103288618954 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064198990
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1198/106186008x321068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064199635
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1214/aop/1176997023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064405770
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1214/aos/1069362388 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064406499
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1214/aos/1176325370 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064406671
137 rdf:type schema:CreativeWork
138 https://doi.org/10.2307/1913242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069640603
139 rdf:type schema:CreativeWork
140 https://www.grid.ac/institutes/grid.263488.3 schema:alternateName Shenzhen University
141 schema:name School of Mathematics and Statistics, Shenzhen University, 518060, Shenzhen, P. R. China
142 rdf:type schema:Organization
143 https://www.grid.ac/institutes/grid.411863.9 schema:alternateName Guangzhou University
144 schema:name School of Economics and Statistics, Guangzhou University, 510006, Guangzhou, P. R. China
145 rdf:type schema:Organization
146 https://www.grid.ac/institutes/grid.469319.0 schema:alternateName Lingnan Normal University
147 schema:name School of Mathematics and Information Science, Guangzhou University, 510006, Guangzhou, P. R. China
148 School of Mathematics and Statistics, Lingnan Normal University, 524048, Zhanjiang, P. R. China
149 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...