Diachronic dialectology: new methods and case studies.
Neither of these kernel functions are used here, although note that the inverse distance function is equivalent to a triangular kernel if HT <math altimg="urn:x-wiley:00791636:media:trps12222:trps12222-math-0006" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>n</mi><mo>=</mo><mn>1</mn></mrow><...
| Published in: | Transactions of the Philological Society Vol. 119; pp. 1 - 251 |
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| Format: | Article |
| Published: |
Wiley-Blackwell
Dec2021 Supplement S1
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=154666029&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 154666029 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00791636 7RD jtl: Transactions of the Philological Society issn: 00791636 maglogo: Y pubinfo: dt: Dec2021 Supplement S1 vid: 119 pid: 480 pub: Wiley-Blackwell artinfo: ui: 154666029 10.1111/1467-968X.12222 ppf: 1 ppct: 250 formats: tig: atl: Diachronic dialectology: new methods and case studies. aug: su: Dialects Sociolinguistics Probability density function Historical linguistics sug: subj: Dialects Sociolinguistics Probability density function Historical linguistics ab: Neither of these kernel functions are used here, although note that the inverse distance function is equivalent to a triangular kernel if HT <math altimg="urn:x-wiley:00791636:media:trps12222:trps12222-math-0006" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>n</mi><mo>=</mo><mn>1</mn></mrow></math> ht . If our variant frequencies at each point are already defined as usage proportions between 0 and 1, then: HT <math altimg="urn:x-wiley:00791636:media:trps12222:trps12222-math-0016" display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mover accent="true"><mi>f</mi><mo> </mo></mover><mfenced separators=" open="(" close=")"><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow></mfenced><mo>=</mo><mfrac><mrow><msubsup><mo> </mo><mrow><mi>l</mi><mo>=</mo><mn>1</mn></mrow><mi>p</mi></msubsup><mi>k</mi><mfenced separators=" open="(" close=")"><mrow><msub><mi>d</mi><mrow><mi>i</mi><mo>,</mo><mi>l</mi></mrow></msub><mo>,</mo><mi>b</mi></mrow></mfenced><msub><mi>X</mi><mrow><mi>j</mi><mo>,</mo><mi>l</mi></mrow></msub></mrow><mrow><msubsup><mo> </mo><mrow><mi>l</mi><mo>=</mo><mn>1</mn></mrow><mi>p</mi></msubsup><mi>k</mi><mfenced separators=" open="(" close=")"><mrow><msub><mi>d</mi><mrow><mi>i</mi><mo>,</mo><mi>l</mi></mrow></msub><mo>,</mo><mi>b</mi></mrow></mfenced></mrow></mfrac><mo>.</mo></mrow></math> ht Having so defined the core of our method, we must determine the kernel function: the importance of points as evidence and therefore the weight assigned to them should fall off with distance, but what should the shape of this decline be? We must first calculate a matrix of distances between all points HT <math altimg="urn:x-wiley:00791636:media:trps12222:trps12222-math-0012" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>D</mi><mrow><mn>1</mn><mi>...</mi><mi>p</mi><mo>,</mo><mn>1</mn><mi>...</mi><mi>p</mi></mrow></msub></math> ht . pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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